{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "scratchpad",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sayakpaul/Training-BatchNorm-and-Only-BatchNorm/blob/master/CIFAR10_Full.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lIYdn1woOS1n",
        "outputId": "ab0f1ae5-1d42-438f-f1ba-07358a733b7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# TensorFlow Imports\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0-rc3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Z8L9GgmRFd_",
        "colab_type": "code",
        "outputId": "807cee8e-1d42-4a70-88f2-59b81d9ac825",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "# Which GPU?\n",
        "!nvidia-smi"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sun May  3 07:35:33 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.64.00    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   36C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-_4ROoYRHJq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget https://raw.githubusercontent.com/GoogleCloudPlatform/keras-idiomatic-programmer/master/zoo/resnet/resnet_cifar10.py"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txX7OoEpROgI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Other imports\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.models import *\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import resnet_cifar10\n",
        "import numpy as np\n",
        "import time\n",
        "\n",
        "# Random seed fixation\n",
        "tf.random.set_seed(666)\n",
        "np.random.seed(666)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NlIHQ-rRWlX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_training_model():\n",
        "    # ResNet20\n",
        "    n = 2\n",
        "    depth =  n * 9 + 2\n",
        "    n_blocks = ((depth - 2) // 9) - 1\n",
        "\n",
        "    # The input tensor\n",
        "    inputs = Input(shape=(32, 32, 3))\n",
        "\n",
        "    # The Stem Convolution Group\n",
        "    x = resnet_cifar10.stem(inputs)\n",
        "\n",
        "    # The learner\n",
        "    x = resnet_cifar10.learner(x, n_blocks)\n",
        "\n",
        "    # The Classifier for 10 classes\n",
        "    outputs = resnet_cifar10.classifier(x, 10)\n",
        "\n",
        "    # Instantiate the Model\n",
        "    model = Model(inputs, outputs)\n",
        "    \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sjq25fUmRY4e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_training(H):\n",
        "\t# construct a plot that plots and saves the training history\n",
        "\twith plt.xkcd():\n",
        "\t\tplt.figure()\n",
        "\t\tplt.plot(H.history[\"loss\"], label=\"train_loss\")\n",
        "\t\tplt.plot(H.history[\"val_loss\"], label=\"val_loss\")\n",
        "\t\tplt.plot(H.history[\"accuracy\"], label=\"train_acc\")\n",
        "\t\tplt.plot(H.history[\"val_accuracy\"], label=\"val_acc\")\n",
        "\t\tplt.title(\"Training Loss and Accuracy\")\n",
        "\t\tplt.xlabel(\"Epoch #\")\n",
        "\t\tplt.ylabel(\"Loss/Accuracy\")\n",
        "\t\tplt.legend(loc=\"lower left\")\n",
        "\t\tplt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jp-VUHKiRqo4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load the training set of CIFAR10\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNhaSmKjR2Vq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BATCH_SIZE = 128\n",
        "\n",
        "def normalize(image, label):\n",
        "    return tf.image.convert_image_dtype(image, tf.float32), label\n",
        "\n",
        "def augment(image,label):\n",
        "    image = tf.image.resize_with_crop_or_pad(image, 40, 40) # Add 8 pixels of padding\n",
        "    image = tf.image.random_crop(image, size=[32, 32, 3]) # Random crop back to 32x32\n",
        "    image = tf.image.random_brightness(image, max_delta=0.5) # Random brightness\n",
        "    image = tf.clip_by_value(image, 0., 1.)\n",
        "    \n",
        "    return image, label\n",
        "\n",
        "train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n",
        "train_ds = (\n",
        "    train_ds\n",
        "    .shuffle(1024)\n",
        "    .map(normalize, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "    .map(augment, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "    .batch(BATCH_SIZE)\n",
        "    .prefetch(tf.data.experimental.AUTOTUNE)\n",
        ")\n",
        "\n",
        "test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test))\n",
        "test_ds = (\n",
        "    test_ds\n",
        "    .map(normalize, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "    .batch(BATCH_SIZE)\n",
        "    .prefetch(tf.data.experimental.AUTOTUNE)\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ymnw4EirV8HT",
        "colab_type": "code",
        "outputId": "34ec56e6-edcd-49f8-a0d9-0c0a0eae0996",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = get_training_model()\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            [(None, 32, 32, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_166 (Conv2D)             (None, 32, 32, 16)   448         input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_164 (BatchN (None, 32, 32, 16)   64          conv2d_166[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_164 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_164[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_168 (Conv2D)             (None, 32, 32, 16)   272         re_lu_164[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_165 (BatchN (None, 32, 32, 16)   64          conv2d_168[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_165 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_165[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_169 (Conv2D)             (None, 32, 32, 16)   2320        re_lu_165[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_166 (BatchN (None, 32, 32, 16)   64          conv2d_169[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_166 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_166[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_170 (Conv2D)             (None, 32, 32, 64)   1088        re_lu_166[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_167 (Conv2D)             (None, 32, 32, 64)   1088        re_lu_164[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_167 (BatchN (None, 32, 32, 64)   256         conv2d_170[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_54 (Add)                    (None, 32, 32, 64)   0           conv2d_167[0][0]                 \n",
            "                                                                 batch_normalization_167[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_167 (ReLU)                (None, 32, 32, 64)   0           add_54[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_171 (Conv2D)             (None, 32, 32, 16)   1040        re_lu_167[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_168 (BatchN (None, 32, 32, 16)   64          conv2d_171[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_168 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_168[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_172 (Conv2D)             (None, 32, 32, 16)   2320        re_lu_168[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_169 (BatchN (None, 32, 32, 16)   64          conv2d_172[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_169 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_169[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_173 (Conv2D)             (None, 32, 32, 64)   1088        re_lu_169[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_170 (BatchN (None, 32, 32, 64)   256         conv2d_173[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_55 (Add)                    (None, 32, 32, 64)   0           batch_normalization_170[0][0]    \n",
            "                                                                 re_lu_167[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_170 (ReLU)                (None, 32, 32, 64)   0           add_55[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_175 (Conv2D)             (None, 32, 32, 64)   4160        re_lu_170[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_171 (BatchN (None, 32, 32, 64)   256         conv2d_175[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_171 (ReLU)                (None, 32, 32, 64)   0           batch_normalization_171[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_176 (Conv2D)             (None, 16, 16, 64)   36928       re_lu_171[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_172 (BatchN (None, 16, 16, 64)   256         conv2d_176[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_172 (ReLU)                (None, 16, 16, 64)   0           batch_normalization_172[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_177 (Conv2D)             (None, 16, 16, 128)  8320        re_lu_172[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_174 (Conv2D)             (None, 16, 16, 128)  8320        re_lu_170[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_173 (BatchN (None, 16, 16, 128)  512         conv2d_177[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_56 (Add)                    (None, 16, 16, 128)  0           conv2d_174[0][0]                 \n",
            "                                                                 batch_normalization_173[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_173 (ReLU)                (None, 16, 16, 128)  0           add_56[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_178 (Conv2D)             (None, 16, 16, 64)   8256        re_lu_173[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_174 (BatchN (None, 16, 16, 64)   256         conv2d_178[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_174 (ReLU)                (None, 16, 16, 64)   0           batch_normalization_174[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_179 (Conv2D)             (None, 16, 16, 64)   36928       re_lu_174[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_175 (BatchN (None, 16, 16, 64)   256         conv2d_179[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_175 (ReLU)                (None, 16, 16, 64)   0           batch_normalization_175[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_180 (Conv2D)             (None, 16, 16, 128)  8320        re_lu_175[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_176 (BatchN (None, 16, 16, 128)  512         conv2d_180[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_57 (Add)                    (None, 16, 16, 128)  0           batch_normalization_176[0][0]    \n",
            "                                                                 re_lu_173[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_176 (ReLU)                (None, 16, 16, 128)  0           add_57[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_182 (Conv2D)             (None, 16, 16, 128)  16512       re_lu_176[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_177 (BatchN (None, 16, 16, 128)  512         conv2d_182[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_177 (ReLU)                (None, 16, 16, 128)  0           batch_normalization_177[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_183 (Conv2D)             (None, 8, 8, 128)    147584      re_lu_177[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_178 (BatchN (None, 8, 8, 128)    512         conv2d_183[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_178 (ReLU)                (None, 8, 8, 128)    0           batch_normalization_178[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_184 (Conv2D)             (None, 8, 8, 256)    33024       re_lu_178[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_181 (Conv2D)             (None, 8, 8, 256)    33024       re_lu_176[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_179 (BatchN (None, 8, 8, 256)    1024        conv2d_184[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_58 (Add)                    (None, 8, 8, 256)    0           conv2d_181[0][0]                 \n",
            "                                                                 batch_normalization_179[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_179 (ReLU)                (None, 8, 8, 256)    0           add_58[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_185 (Conv2D)             (None, 8, 8, 128)    32896       re_lu_179[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_180 (BatchN (None, 8, 8, 128)    512         conv2d_185[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_180 (ReLU)                (None, 8, 8, 128)    0           batch_normalization_180[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_186 (Conv2D)             (None, 8, 8, 128)    147584      re_lu_180[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_181 (BatchN (None, 8, 8, 128)    512         conv2d_186[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_181 (ReLU)                (None, 8, 8, 128)    0           batch_normalization_181[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_187 (Conv2D)             (None, 8, 8, 256)    33024       re_lu_181[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_182 (BatchN (None, 8, 8, 256)    1024        conv2d_187[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_59 (Add)                    (None, 8, 8, 256)    0           batch_normalization_182[0][0]    \n",
            "                                                                 re_lu_179[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_182 (ReLU)                (None, 8, 8, 256)    0           add_59[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_183 (BatchN (None, 8, 8, 256)    1024        re_lu_182[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_183 (ReLU)                (None, 8, 8, 256)    0           batch_normalization_183[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, 1, 1, 256)    0           re_lu_183[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)             (None, 256)          0           average_pooling2d_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 10)           2570        flatten_1[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 575,114\n",
            "Trainable params: 571,114\n",
            "Non-trainable params: 4,000\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lNCYm8J2WiMh",
        "colab_type": "text"
      },
      "source": [
        "- Total params: 575,114\n",
        "- Trainable params: 571,114\n",
        "- Non-trainable params: 4,000\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOv6rFsbR9z2",
        "colab_type": "code",
        "outputId": "7f74f871-4ea6-4322-cc77-bc17f6c4a8a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Train model\n",
        "model = get_training_model()\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"sgd\", metrics=[\"accuracy\"])\n",
        "start = time.time()\n",
        "h = model.fit(train_ds,\n",
        "         validation_data=test_ds,\n",
        "         epochs=75)\n",
        "end = time.time()\n",
        "print(\"Network takes {:.3f} seconds to train\".format(end - start))\n",
        "plot_training(h)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/75\n",
            "391/391 [==============================] - 15s 39ms/step - loss: 2.1497 - accuracy: 0.2026 - val_loss: 2.0214 - val_accuracy: 0.2275\n",
            "Epoch 2/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.9578 - accuracy: 0.2725 - val_loss: 1.8603 - val_accuracy: 0.3016\n",
            "Epoch 3/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.8590 - accuracy: 0.3065 - val_loss: 1.7817 - val_accuracy: 0.3285\n",
            "Epoch 4/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.8006 - accuracy: 0.3270 - val_loss: 1.7104 - val_accuracy: 0.3516\n",
            "Epoch 5/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.7557 - accuracy: 0.3440 - val_loss: 1.7278 - val_accuracy: 0.3506\n",
            "Epoch 6/75\n",
            "391/391 [==============================] - 15s 37ms/step - loss: 1.7251 - accuracy: 0.3571 - val_loss: 1.6611 - val_accuracy: 0.3748\n",
            "Epoch 7/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.6976 - accuracy: 0.3679 - val_loss: 1.6150 - val_accuracy: 0.3982\n",
            "Epoch 8/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.6721 - accuracy: 0.3807 - val_loss: 1.6553 - val_accuracy: 0.3847\n",
            "Epoch 9/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.6444 - accuracy: 0.3937 - val_loss: 1.7110 - val_accuracy: 0.3796\n",
            "Epoch 10/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.6144 - accuracy: 0.4060 - val_loss: 1.5467 - val_accuracy: 0.4284\n",
            "Epoch 11/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.5881 - accuracy: 0.4167 - val_loss: 1.5303 - val_accuracy: 0.4324\n",
            "Epoch 12/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.5591 - accuracy: 0.4291 - val_loss: 1.4795 - val_accuracy: 0.4547\n",
            "Epoch 13/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.5328 - accuracy: 0.4401 - val_loss: 1.4600 - val_accuracy: 0.4616\n",
            "Epoch 14/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.5098 - accuracy: 0.4482 - val_loss: 1.4451 - val_accuracy: 0.4690\n",
            "Epoch 15/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.4817 - accuracy: 0.4608 - val_loss: 1.5113 - val_accuracy: 0.4563\n",
            "Epoch 16/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.4615 - accuracy: 0.4672 - val_loss: 1.3955 - val_accuracy: 0.4874\n",
            "Epoch 17/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.4387 - accuracy: 0.4771 - val_loss: 1.3764 - val_accuracy: 0.5041\n",
            "Epoch 18/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.4141 - accuracy: 0.4877 - val_loss: 1.3710 - val_accuracy: 0.5067\n",
            "Epoch 19/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.3932 - accuracy: 0.4950 - val_loss: 1.3515 - val_accuracy: 0.5060\n",
            "Epoch 20/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.3771 - accuracy: 0.5025 - val_loss: 1.3064 - val_accuracy: 0.5265\n",
            "Epoch 21/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.3616 - accuracy: 0.5099 - val_loss: 1.2930 - val_accuracy: 0.5301\n",
            "Epoch 22/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.3394 - accuracy: 0.5191 - val_loss: 1.3137 - val_accuracy: 0.5297\n",
            "Epoch 23/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.3182 - accuracy: 0.5256 - val_loss: 1.2674 - val_accuracy: 0.5394\n",
            "Epoch 24/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.3024 - accuracy: 0.5308 - val_loss: 1.2609 - val_accuracy: 0.5432\n",
            "Epoch 25/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.2863 - accuracy: 0.5346 - val_loss: 1.2288 - val_accuracy: 0.5565\n",
            "Epoch 26/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.2685 - accuracy: 0.5449 - val_loss: 1.2151 - val_accuracy: 0.5708\n",
            "Epoch 27/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.2581 - accuracy: 0.5485 - val_loss: 1.2102 - val_accuracy: 0.5648\n",
            "Epoch 28/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.2371 - accuracy: 0.5546 - val_loss: 1.1673 - val_accuracy: 0.5824\n",
            "Epoch 29/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.2226 - accuracy: 0.5593 - val_loss: 1.1849 - val_accuracy: 0.5756\n",
            "Epoch 30/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.2069 - accuracy: 0.5664 - val_loss: 1.1440 - val_accuracy: 0.5877\n",
            "Epoch 31/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.1952 - accuracy: 0.5704 - val_loss: 1.1672 - val_accuracy: 0.5856\n",
            "Epoch 32/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.1798 - accuracy: 0.5776 - val_loss: 1.1167 - val_accuracy: 0.6009\n",
            "Epoch 33/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.1650 - accuracy: 0.5814 - val_loss: 1.0924 - val_accuracy: 0.6065\n",
            "Epoch 34/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.1520 - accuracy: 0.5876 - val_loss: 1.1483 - val_accuracy: 0.5864\n",
            "Epoch 35/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.1383 - accuracy: 0.5892 - val_loss: 1.0888 - val_accuracy: 0.6112\n",
            "Epoch 36/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.1277 - accuracy: 0.5972 - val_loss: 1.1015 - val_accuracy: 0.6063\n",
            "Epoch 37/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.1133 - accuracy: 0.6002 - val_loss: 1.1473 - val_accuracy: 0.5931\n",
            "Epoch 38/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.1046 - accuracy: 0.6041 - val_loss: 1.0546 - val_accuracy: 0.6216\n",
            "Epoch 39/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.0917 - accuracy: 0.6084 - val_loss: 1.0758 - val_accuracy: 0.6217\n",
            "Epoch 40/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.0791 - accuracy: 0.6150 - val_loss: 1.0306 - val_accuracy: 0.6321\n",
            "Epoch 41/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.0701 - accuracy: 0.6178 - val_loss: 1.0176 - val_accuracy: 0.6333\n",
            "Epoch 42/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.0572 - accuracy: 0.6227 - val_loss: 1.0605 - val_accuracy: 0.6253\n",
            "Epoch 43/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.0525 - accuracy: 0.6229 - val_loss: 1.2080 - val_accuracy: 0.5831\n",
            "Epoch 44/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.0408 - accuracy: 0.6262 - val_loss: 0.9815 - val_accuracy: 0.6489\n",
            "Epoch 45/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.0314 - accuracy: 0.6328 - val_loss: 0.9661 - val_accuracy: 0.6556\n",
            "Epoch 46/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.0211 - accuracy: 0.6336 - val_loss: 0.9845 - val_accuracy: 0.6498\n",
            "Epoch 47/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.0104 - accuracy: 0.6383 - val_loss: 1.0018 - val_accuracy: 0.6418\n",
            "Epoch 48/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.0058 - accuracy: 0.6423 - val_loss: 0.9782 - val_accuracy: 0.6501\n",
            "Epoch 49/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.9938 - accuracy: 0.6455 - val_loss: 0.9559 - val_accuracy: 0.6589\n",
            "Epoch 50/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.9881 - accuracy: 0.6491 - val_loss: 0.9590 - val_accuracy: 0.6557\n",
            "Epoch 51/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.9782 - accuracy: 0.6507 - val_loss: 0.9784 - val_accuracy: 0.6490\n",
            "Epoch 52/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.9715 - accuracy: 0.6545 - val_loss: 0.9350 - val_accuracy: 0.6647\n",
            "Epoch 53/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.9610 - accuracy: 0.6574 - val_loss: 0.9060 - val_accuracy: 0.6737\n",
            "Epoch 54/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.9587 - accuracy: 0.6591 - val_loss: 0.9295 - val_accuracy: 0.6687\n",
            "Epoch 55/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.9492 - accuracy: 0.6616 - val_loss: 0.9871 - val_accuracy: 0.6541\n",
            "Epoch 56/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.9412 - accuracy: 0.6660 - val_loss: 0.9116 - val_accuracy: 0.6759\n",
            "Epoch 57/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.9334 - accuracy: 0.6677 - val_loss: 0.8995 - val_accuracy: 0.6809\n",
            "Epoch 58/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.9232 - accuracy: 0.6721 - val_loss: 0.8925 - val_accuracy: 0.6865\n",
            "Epoch 59/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.9203 - accuracy: 0.6732 - val_loss: 0.8801 - val_accuracy: 0.6872\n",
            "Epoch 60/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.9145 - accuracy: 0.6758 - val_loss: 0.8877 - val_accuracy: 0.6827\n",
            "Epoch 61/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.9041 - accuracy: 0.6804 - val_loss: 0.9164 - val_accuracy: 0.6759\n",
            "Epoch 62/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8973 - accuracy: 0.6796 - val_loss: 0.8995 - val_accuracy: 0.6795\n",
            "Epoch 63/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8939 - accuracy: 0.6816 - val_loss: 0.8941 - val_accuracy: 0.6828\n",
            "Epoch 64/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8792 - accuracy: 0.6888 - val_loss: 0.8610 - val_accuracy: 0.6934\n",
            "Epoch 65/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8771 - accuracy: 0.6892 - val_loss: 0.9018 - val_accuracy: 0.6835\n",
            "Epoch 66/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8693 - accuracy: 0.6923 - val_loss: 0.9029 - val_accuracy: 0.6813\n",
            "Epoch 67/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8633 - accuracy: 0.6950 - val_loss: 0.8358 - val_accuracy: 0.6997\n",
            "Epoch 68/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8593 - accuracy: 0.6963 - val_loss: 0.8712 - val_accuracy: 0.6904\n",
            "Epoch 69/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8553 - accuracy: 0.6968 - val_loss: 0.8614 - val_accuracy: 0.6955\n",
            "Epoch 70/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8484 - accuracy: 0.6986 - val_loss: 0.8723 - val_accuracy: 0.6934\n",
            "Epoch 71/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8412 - accuracy: 0.7030 - val_loss: 0.8392 - val_accuracy: 0.7028\n",
            "Epoch 72/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8352 - accuracy: 0.7040 - val_loss: 0.8880 - val_accuracy: 0.6887\n",
            "Epoch 73/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8322 - accuracy: 0.7040 - val_loss: 0.8086 - val_accuracy: 0.7175\n",
            "Epoch 74/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8247 - accuracy: 0.7082 - val_loss: 0.8670 - val_accuracy: 0.6969\n",
            "Epoch 75/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 0.8141 - accuracy: 0.7133 - val_loss: 0.8391 - val_accuracy: 0.7041\n",
            "Network takes 1073.722 seconds to train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXBgHyMLggnE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save the weights to GCS bucket for reproducibility\n",
        "PROJECT_ID = \"fast-ai-exploration\" \n",
        "!gcloud config set project $PROJECT_ID\n",
        "\n",
        "from google.colab import auth as google_auth\n",
        "google_auth.authenticate_user()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AT1eEEDXiFKT",
        "colab_type": "code",
        "outputId": "f1c8771a-fe46-4d38-9067-a0092dd49f25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!gsutil mb gs://batch_norm"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating gs://batch_norm/...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5g9rjAjhWua",
        "colab_type": "code",
        "outputId": "228b1948-e146-41f7-bb0b-a06d27cec3b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "model.save(\"vanilla_resnet\")\n",
        "!gsutil -m cp -r vanilla_resnet gs://batch_norm"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "INFO:tensorflow:Assets written to: vanilla_resnet/assets\n",
            "Copying file://vanilla_resnet/saved_model.pb [Content-Type=application/octet-stream]...\n",
            "Copying file://vanilla_resnet/variables/variables.data-00000-of-00002 [Content-Type=application/octet-stream]...\n",
            "Copying file://vanilla_resnet/variables/variables.index [Content-Type=application/octet-stream]...\n",
            "Copying file://vanilla_resnet/variables/variables.data-00001-of-00002 [Content-Type=application/octet-stream]...\n",
            "/ [4/4 files][  3.9 MiB/  3.9 MiB] 100% Done                                    \n",
            "Operation completed over 4 objects/3.9 MiB.                                      \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rM5mFUHkSWcp",
        "colab_type": "code",
        "outputId": "6f63e893-9a38-47b3-83ce-f3e752a5af52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Train model with a decay schedule\n",
        "first_decay_steps = 1000\n",
        "lr_decayed_fn = (\n",
        "  tf.keras.experimental.CosineDecay(\n",
        "      initial_learning_rate=0.1,\n",
        "      decay_steps=first_decay_steps))\n",
        "\n",
        "model = get_training_model()\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=tf.keras.optimizers.SGD(lr_decayed_fn), metrics=[\"accuracy\"])\n",
        "start = time.time()\n",
        "h = model.fit(train_ds,\n",
        "         validation_data=test_ds,\n",
        "         epochs=75)\n",
        "end = time.time()\n",
        "print(\"Network takes {:.3f} seconds to train\".format(end - start))\n",
        "plot_training(h)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.8414 - accuracy: 0.3135 - val_loss: 1.7982 - val_accuracy: 0.3394\n",
            "Epoch 2/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5850 - accuracy: 0.4167 - val_loss: 1.4672 - val_accuracy: 0.4633\n",
            "Epoch 3/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5165 - accuracy: 0.4494 - val_loss: 1.4210 - val_accuracy: 0.4781\n",
            "Epoch 4/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5117 - accuracy: 0.4509 - val_loss: 1.4210 - val_accuracy: 0.4783\n",
            "Epoch 5/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5094 - accuracy: 0.4516 - val_loss: 1.4210 - val_accuracy: 0.4785\n",
            "Epoch 6/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5095 - accuracy: 0.4491 - val_loss: 1.4209 - val_accuracy: 0.4787\n",
            "Epoch 7/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5150 - accuracy: 0.4489 - val_loss: 1.4210 - val_accuracy: 0.4779\n",
            "Epoch 8/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5115 - accuracy: 0.4493 - val_loss: 1.4207 - val_accuracy: 0.4782\n",
            "Epoch 9/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5164 - accuracy: 0.4469 - val_loss: 1.4208 - val_accuracy: 0.4784\n",
            "Epoch 10/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5150 - accuracy: 0.4477 - val_loss: 1.4210 - val_accuracy: 0.4791\n",
            "Epoch 11/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5114 - accuracy: 0.4483 - val_loss: 1.4210 - val_accuracy: 0.4781\n",
            "Epoch 12/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5131 - accuracy: 0.4492 - val_loss: 1.4209 - val_accuracy: 0.4782\n",
            "Epoch 13/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5140 - accuracy: 0.4479 - val_loss: 1.4210 - val_accuracy: 0.4780\n",
            "Epoch 14/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5114 - accuracy: 0.4525 - val_loss: 1.4210 - val_accuracy: 0.4785\n",
            "Epoch 15/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5108 - accuracy: 0.4511 - val_loss: 1.4210 - val_accuracy: 0.4781\n",
            "Epoch 16/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5109 - accuracy: 0.4497 - val_loss: 1.4212 - val_accuracy: 0.4788\n",
            "Epoch 17/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5105 - accuracy: 0.4512 - val_loss: 1.4209 - val_accuracy: 0.4776\n",
            "Epoch 18/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5145 - accuracy: 0.4482 - val_loss: 1.4209 - val_accuracy: 0.4784\n",
            "Epoch 19/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5107 - accuracy: 0.4504 - val_loss: 1.4210 - val_accuracy: 0.4789\n",
            "Epoch 20/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5104 - accuracy: 0.4499 - val_loss: 1.4210 - val_accuracy: 0.4784\n",
            "Epoch 21/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5126 - accuracy: 0.4526 - val_loss: 1.4209 - val_accuracy: 0.4783\n",
            "Epoch 22/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5102 - accuracy: 0.4514 - val_loss: 1.4210 - val_accuracy: 0.4779\n",
            "Epoch 23/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5127 - accuracy: 0.4489 - val_loss: 1.4209 - val_accuracy: 0.4788\n",
            "Epoch 24/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5139 - accuracy: 0.4498 - val_loss: 1.4211 - val_accuracy: 0.4786\n",
            "Epoch 25/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5116 - accuracy: 0.4502 - val_loss: 1.4209 - val_accuracy: 0.4788\n",
            "Epoch 26/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5162 - accuracy: 0.4487 - val_loss: 1.4211 - val_accuracy: 0.4787\n",
            "Epoch 27/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5128 - accuracy: 0.4487 - val_loss: 1.4209 - val_accuracy: 0.4784\n",
            "Epoch 28/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5147 - accuracy: 0.4497 - val_loss: 1.4210 - val_accuracy: 0.4778\n",
            "Epoch 29/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5115 - accuracy: 0.4490 - val_loss: 1.4207 - val_accuracy: 0.4782\n",
            "Epoch 30/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5124 - accuracy: 0.4507 - val_loss: 1.4209 - val_accuracy: 0.4780\n",
            "Epoch 31/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5131 - accuracy: 0.4477 - val_loss: 1.4209 - val_accuracy: 0.4782\n",
            "Epoch 32/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5111 - accuracy: 0.4512 - val_loss: 1.4210 - val_accuracy: 0.4789\n",
            "Epoch 33/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5102 - accuracy: 0.4510 - val_loss: 1.4210 - val_accuracy: 0.4780\n",
            "Epoch 34/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5109 - accuracy: 0.4492 - val_loss: 1.4210 - val_accuracy: 0.4783\n",
            "Epoch 35/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5140 - accuracy: 0.4484 - val_loss: 1.4210 - val_accuracy: 0.4781\n",
            "Epoch 36/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5108 - accuracy: 0.4491 - val_loss: 1.4210 - val_accuracy: 0.4784\n",
            "Epoch 37/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5173 - accuracy: 0.4459 - val_loss: 1.4210 - val_accuracy: 0.4787\n",
            "Epoch 38/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5109 - accuracy: 0.4513 - val_loss: 1.4211 - val_accuracy: 0.4785\n",
            "Epoch 39/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5150 - accuracy: 0.4453 - val_loss: 1.4209 - val_accuracy: 0.4783\n",
            "Epoch 40/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5116 - accuracy: 0.4490 - val_loss: 1.4209 - val_accuracy: 0.4781\n",
            "Epoch 41/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5123 - accuracy: 0.4488 - val_loss: 1.4209 - val_accuracy: 0.4785\n",
            "Epoch 42/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5161 - accuracy: 0.4492 - val_loss: 1.4208 - val_accuracy: 0.4785\n",
            "Epoch 43/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5107 - accuracy: 0.4486 - val_loss: 1.4209 - val_accuracy: 0.4782\n",
            "Epoch 44/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5096 - accuracy: 0.4520 - val_loss: 1.4209 - val_accuracy: 0.4782\n",
            "Epoch 45/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5154 - accuracy: 0.4482 - val_loss: 1.4211 - val_accuracy: 0.4781\n",
            "Epoch 46/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5107 - accuracy: 0.4509 - val_loss: 1.4210 - val_accuracy: 0.4782\n",
            "Epoch 47/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5122 - accuracy: 0.4498 - val_loss: 1.4211 - val_accuracy: 0.4785\n",
            "Epoch 48/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5131 - accuracy: 0.4501 - val_loss: 1.4209 - val_accuracy: 0.4777\n",
            "Epoch 49/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5122 - accuracy: 0.4485 - val_loss: 1.4210 - val_accuracy: 0.4781\n",
            "Epoch 50/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5086 - accuracy: 0.4518 - val_loss: 1.4210 - val_accuracy: 0.4787\n",
            "Epoch 51/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5111 - accuracy: 0.4513 - val_loss: 1.4210 - val_accuracy: 0.4785\n",
            "Epoch 52/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5116 - accuracy: 0.4483 - val_loss: 1.4209 - val_accuracy: 0.4783\n",
            "Epoch 53/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5136 - accuracy: 0.4500 - val_loss: 1.4208 - val_accuracy: 0.4787\n",
            "Epoch 54/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5136 - accuracy: 0.4491 - val_loss: 1.4209 - val_accuracy: 0.4783\n",
            "Epoch 55/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5128 - accuracy: 0.4500 - val_loss: 1.4209 - val_accuracy: 0.4784\n",
            "Epoch 56/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5168 - accuracy: 0.4469 - val_loss: 1.4211 - val_accuracy: 0.4783\n",
            "Epoch 57/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5125 - accuracy: 0.4489 - val_loss: 1.4210 - val_accuracy: 0.4779\n",
            "Epoch 58/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5122 - accuracy: 0.4501 - val_loss: 1.4207 - val_accuracy: 0.4782\n",
            "Epoch 59/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5091 - accuracy: 0.4507 - val_loss: 1.4210 - val_accuracy: 0.4784\n",
            "Epoch 60/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5119 - accuracy: 0.4503 - val_loss: 1.4210 - val_accuracy: 0.4776\n",
            "Epoch 61/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5145 - accuracy: 0.4473 - val_loss: 1.4210 - val_accuracy: 0.4780\n",
            "Epoch 62/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5114 - accuracy: 0.4506 - val_loss: 1.4208 - val_accuracy: 0.4786\n",
            "Epoch 63/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5121 - accuracy: 0.4509 - val_loss: 1.4208 - val_accuracy: 0.4784\n",
            "Epoch 64/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5143 - accuracy: 0.4497 - val_loss: 1.4210 - val_accuracy: 0.4780\n",
            "Epoch 65/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5108 - accuracy: 0.4508 - val_loss: 1.4210 - val_accuracy: 0.4777\n",
            "Epoch 66/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5100 - accuracy: 0.4509 - val_loss: 1.4211 - val_accuracy: 0.4797\n",
            "Epoch 67/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5152 - accuracy: 0.4486 - val_loss: 1.4212 - val_accuracy: 0.4784\n",
            "Epoch 68/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5135 - accuracy: 0.4481 - val_loss: 1.4209 - val_accuracy: 0.4785\n",
            "Epoch 69/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5127 - accuracy: 0.4494 - val_loss: 1.4209 - val_accuracy: 0.4783\n",
            "Epoch 70/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5136 - accuracy: 0.4476 - val_loss: 1.4210 - val_accuracy: 0.4784\n",
            "Epoch 71/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5130 - accuracy: 0.4502 - val_loss: 1.4209 - val_accuracy: 0.4785\n",
            "Epoch 72/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5121 - accuracy: 0.4510 - val_loss: 1.4210 - val_accuracy: 0.4783\n",
            "Epoch 73/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5106 - accuracy: 0.4527 - val_loss: 1.4209 - val_accuracy: 0.4787\n",
            "Epoch 74/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5125 - accuracy: 0.4478 - val_loss: 1.4209 - val_accuracy: 0.4785\n",
            "Epoch 75/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.5114 - accuracy: 0.4489 - val_loss: 1.4209 - val_accuracy: 0.4787\n",
            "Network takes 1066.445 seconds to train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5o1kTTj8iUL3",
        "colab_type": "code",
        "outputId": "cfb99171-033e-41cf-b8bf-ee0c0a55bc4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "model.save(\"resnet_lrsch_1\")\n",
        "!gsutil -m cp -r resnet_lrsch_1 gs://batch_norm"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: resnet_lrsch_1/assets\n",
            "Copying file://resnet_lrsch_1/saved_model.pb [Content-Type=application/octet-stream]...\n",
            "Copying file://resnet_lrsch_1/variables/variables.data-00000-of-00002 [Content-Type=application/octet-stream]...\n",
            "Copying file://resnet_lrsch_1/variables/variables.data-00001-of-00002 [Content-Type=application/octet-stream]...\n",
            "Copying file://resnet_lrsch_1/variables/variables.index [Content-Type=application/octet-stream]...\n",
            "/ [4/4 files][  3.9 MiB/  3.9 MiB] 100% Done                                    \n",
            "Operation completed over 4 objects/3.9 MiB.                                      \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLFNETnFZL_J",
        "colab_type": "code",
        "outputId": "62ddd014-5179-4938-b9b2-4da4372c2b56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Train model with a different decay schedule\n",
        "first_decay_steps = 1000\n",
        "lr_decayed_fn = (\n",
        "  tf.keras.experimental.LinearCosineDecay(\n",
        "      initial_learning_rate=0.1,\n",
        "      decay_steps=first_decay_steps))\n",
        "\n",
        "model = get_training_model()\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=tf.keras.optimizers.SGD(lr_decayed_fn), metrics=[\"accuracy\"])\n",
        "start = time.time()\n",
        "h = model.fit(train_ds,\n",
        "         validation_data=test_ds,\n",
        "         epochs=75)\n",
        "end = time.time()\n",
        "print(\"Network takes {:.3f} seconds to train\".format(end - start))\n",
        "plot_training(h)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/75\n",
            "391/391 [==============================] - 14s 37ms/step - loss: 1.8823 - accuracy: 0.2932 - val_loss: 1.8831 - val_accuracy: 0.2946\n",
            "Epoch 2/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6850 - accuracy: 0.3755 - val_loss: 1.5842 - val_accuracy: 0.4137\n",
            "Epoch 3/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6551 - accuracy: 0.3878 - val_loss: 1.5780 - val_accuracy: 0.4184\n",
            "Epoch 4/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6546 - accuracy: 0.3897 - val_loss: 1.5774 - val_accuracy: 0.4185\n",
            "Epoch 5/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6536 - accuracy: 0.3894 - val_loss: 1.5774 - val_accuracy: 0.4180\n",
            "Epoch 6/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6533 - accuracy: 0.3893 - val_loss: 1.5774 - val_accuracy: 0.4191\n",
            "Epoch 7/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6493 - accuracy: 0.3909 - val_loss: 1.5771 - val_accuracy: 0.4196\n",
            "Epoch 8/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6525 - accuracy: 0.3907 - val_loss: 1.5772 - val_accuracy: 0.4193\n",
            "Epoch 9/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6549 - accuracy: 0.3878 - val_loss: 1.5769 - val_accuracy: 0.4197\n",
            "Epoch 10/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6502 - accuracy: 0.3898 - val_loss: 1.5764 - val_accuracy: 0.4200\n",
            "Epoch 11/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6512 - accuracy: 0.3889 - val_loss: 1.5762 - val_accuracy: 0.4204\n",
            "Epoch 12/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6515 - accuracy: 0.3893 - val_loss: 1.5753 - val_accuracy: 0.4210\n",
            "Epoch 13/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6501 - accuracy: 0.3907 - val_loss: 1.5751 - val_accuracy: 0.4201\n",
            "Epoch 14/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6532 - accuracy: 0.3913 - val_loss: 1.5746 - val_accuracy: 0.4198\n",
            "Epoch 15/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6493 - accuracy: 0.3911 - val_loss: 1.5748 - val_accuracy: 0.4203\n",
            "Epoch 16/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6512 - accuracy: 0.3905 - val_loss: 1.5743 - val_accuracy: 0.4199\n",
            "Epoch 17/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6479 - accuracy: 0.3912 - val_loss: 1.5737 - val_accuracy: 0.4203\n",
            "Epoch 18/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6497 - accuracy: 0.3922 - val_loss: 1.5736 - val_accuracy: 0.4201\n",
            "Epoch 19/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6487 - accuracy: 0.3917 - val_loss: 1.5732 - val_accuracy: 0.4199\n",
            "Epoch 20/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6491 - accuracy: 0.3907 - val_loss: 1.5723 - val_accuracy: 0.4206\n",
            "Epoch 21/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6485 - accuracy: 0.3921 - val_loss: 1.5722 - val_accuracy: 0.4197\n",
            "Epoch 22/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6478 - accuracy: 0.3934 - val_loss: 1.5718 - val_accuracy: 0.4203\n",
            "Epoch 23/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6452 - accuracy: 0.3917 - val_loss: 1.5722 - val_accuracy: 0.4203\n",
            "Epoch 24/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6468 - accuracy: 0.3902 - val_loss: 1.5716 - val_accuracy: 0.4206\n",
            "Epoch 25/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6458 - accuracy: 0.3919 - val_loss: 1.5715 - val_accuracy: 0.4206\n",
            "Epoch 26/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6475 - accuracy: 0.3914 - val_loss: 1.5711 - val_accuracy: 0.4208\n",
            "Epoch 27/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6461 - accuracy: 0.3911 - val_loss: 1.5708 - val_accuracy: 0.4213\n",
            "Epoch 28/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6463 - accuracy: 0.3919 - val_loss: 1.5699 - val_accuracy: 0.4221\n",
            "Epoch 29/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6461 - accuracy: 0.3901 - val_loss: 1.5698 - val_accuracy: 0.4214\n",
            "Epoch 30/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6469 - accuracy: 0.3902 - val_loss: 1.5695 - val_accuracy: 0.4222\n",
            "Epoch 31/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6441 - accuracy: 0.3924 - val_loss: 1.5697 - val_accuracy: 0.4229\n",
            "Epoch 32/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6435 - accuracy: 0.3921 - val_loss: 1.5693 - val_accuracy: 0.4223\n",
            "Epoch 33/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6409 - accuracy: 0.3949 - val_loss: 1.5691 - val_accuracy: 0.4235\n",
            "Epoch 34/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6446 - accuracy: 0.3934 - val_loss: 1.5682 - val_accuracy: 0.4235\n",
            "Epoch 35/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6452 - accuracy: 0.3918 - val_loss: 1.5680 - val_accuracy: 0.4225\n",
            "Epoch 36/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6463 - accuracy: 0.3924 - val_loss: 1.5675 - val_accuracy: 0.4233\n",
            "Epoch 37/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6437 - accuracy: 0.3936 - val_loss: 1.5671 - val_accuracy: 0.4236\n",
            "Epoch 38/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6377 - accuracy: 0.3947 - val_loss: 1.5665 - val_accuracy: 0.4236\n",
            "Epoch 39/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6407 - accuracy: 0.3963 - val_loss: 1.5667 - val_accuracy: 0.4245\n",
            "Epoch 40/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6433 - accuracy: 0.3952 - val_loss: 1.5666 - val_accuracy: 0.4244\n",
            "Epoch 41/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6391 - accuracy: 0.3959 - val_loss: 1.5660 - val_accuracy: 0.4252\n",
            "Epoch 42/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6420 - accuracy: 0.3937 - val_loss: 1.5657 - val_accuracy: 0.4241\n",
            "Epoch 43/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6401 - accuracy: 0.3960 - val_loss: 1.5653 - val_accuracy: 0.4239\n",
            "Epoch 44/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6403 - accuracy: 0.3957 - val_loss: 1.5651 - val_accuracy: 0.4233\n",
            "Epoch 45/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6368 - accuracy: 0.3950 - val_loss: 1.5647 - val_accuracy: 0.4239\n",
            "Epoch 46/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6385 - accuracy: 0.3952 - val_loss: 1.5642 - val_accuracy: 0.4242\n",
            "Epoch 47/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6409 - accuracy: 0.3940 - val_loss: 1.5638 - val_accuracy: 0.4246\n",
            "Epoch 48/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6414 - accuracy: 0.3921 - val_loss: 1.5633 - val_accuracy: 0.4254\n",
            "Epoch 49/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6367 - accuracy: 0.3981 - val_loss: 1.5634 - val_accuracy: 0.4253\n",
            "Epoch 50/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6381 - accuracy: 0.3978 - val_loss: 1.5625 - val_accuracy: 0.4242\n",
            "Epoch 51/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6383 - accuracy: 0.3968 - val_loss: 1.5619 - val_accuracy: 0.4262\n",
            "Epoch 52/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6379 - accuracy: 0.3958 - val_loss: 1.5622 - val_accuracy: 0.4244\n",
            "Epoch 53/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6352 - accuracy: 0.3961 - val_loss: 1.5618 - val_accuracy: 0.4253\n",
            "Epoch 54/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6367 - accuracy: 0.3961 - val_loss: 1.5616 - val_accuracy: 0.4257\n",
            "Epoch 55/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6382 - accuracy: 0.3943 - val_loss: 1.5611 - val_accuracy: 0.4262\n",
            "Epoch 56/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6364 - accuracy: 0.3963 - val_loss: 1.5608 - val_accuracy: 0.4271\n",
            "Epoch 57/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6329 - accuracy: 0.3996 - val_loss: 1.5606 - val_accuracy: 0.4264\n",
            "Epoch 58/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6377 - accuracy: 0.3970 - val_loss: 1.5607 - val_accuracy: 0.4263\n",
            "Epoch 59/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6350 - accuracy: 0.3987 - val_loss: 1.5592 - val_accuracy: 0.4267\n",
            "Epoch 60/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6370 - accuracy: 0.3974 - val_loss: 1.5594 - val_accuracy: 0.4262\n",
            "Epoch 61/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6333 - accuracy: 0.3968 - val_loss: 1.5586 - val_accuracy: 0.4276\n",
            "Epoch 62/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6345 - accuracy: 0.3965 - val_loss: 1.5587 - val_accuracy: 0.4264\n",
            "Epoch 63/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6317 - accuracy: 0.4007 - val_loss: 1.5577 - val_accuracy: 0.4271\n",
            "Epoch 64/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6349 - accuracy: 0.3975 - val_loss: 1.5577 - val_accuracy: 0.4263\n",
            "Epoch 65/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6330 - accuracy: 0.3973 - val_loss: 1.5577 - val_accuracy: 0.4265\n",
            "Epoch 66/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6293 - accuracy: 0.3967 - val_loss: 1.5571 - val_accuracy: 0.4263\n",
            "Epoch 67/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6349 - accuracy: 0.3972 - val_loss: 1.5569 - val_accuracy: 0.4274\n",
            "Epoch 68/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6328 - accuracy: 0.3996 - val_loss: 1.5565 - val_accuracy: 0.4273\n",
            "Epoch 69/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6322 - accuracy: 0.3987 - val_loss: 1.5561 - val_accuracy: 0.4266\n",
            "Epoch 70/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6325 - accuracy: 0.3954 - val_loss: 1.5558 - val_accuracy: 0.4266\n",
            "Epoch 71/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6316 - accuracy: 0.3978 - val_loss: 1.5549 - val_accuracy: 0.4275\n",
            "Epoch 72/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6318 - accuracy: 0.3972 - val_loss: 1.5545 - val_accuracy: 0.4267\n",
            "Epoch 73/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6311 - accuracy: 0.4006 - val_loss: 1.5546 - val_accuracy: 0.4273\n",
            "Epoch 74/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6330 - accuracy: 0.3988 - val_loss: 1.5546 - val_accuracy: 0.4267\n",
            "Epoch 75/75\n",
            "391/391 [==============================] - 14s 36ms/step - loss: 1.6309 - accuracy: 0.3996 - val_loss: 1.5540 - val_accuracy: 0.4268\n",
            "Network takes 1066.823 seconds to train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2GI0ADGIiil1",
        "colab_type": "code",
        "outputId": "c113f9ca-ce44-4057-ddea-4bf48882f1e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "model.save(\"resnet_lrsch_2\")\n",
        "!gsutil -m cp -r resnet_lrsch_2 gs://batch_norm"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: resnet_lrsch_2/assets\n",
            "Copying file://resnet_lrsch_2/saved_model.pb [Content-Type=application/octet-stream]...\n",
            "Copying file://resnet_lrsch_2/variables/variables.data-00001-of-00002 [Content-Type=application/octet-stream]...\n",
            "Copying file://resnet_lrsch_2/variables/variables.index [Content-Type=application/octet-stream]...\n",
            "Copying file://resnet_lrsch_2/variables/variables.data-00000-of-00002 [Content-Type=application/octet-stream]...\n",
            "/ [4/4 files][  3.9 MiB/  3.9 MiB] 100% Done                                    \n",
            "Operation completed over 4 objects/3.9 MiB.                                      \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ou8GN5VhWrbd",
        "colab_type": "code",
        "outputId": "6f15a991-69d1-4fd7-959e-e9087018d6b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = get_training_model()\n",
        "\n",
        "for layer in model.layers:\n",
        "    if not isinstance(layer, tf.keras.layers.BatchNormalization):\n",
        "        if hasattr(layer, \"trainable\"):\n",
        "            layer.trainable=False\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_5\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_6 (InputLayer)            [(None, 32, 32, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_254 (Conv2D)             (None, 32, 32, 16)   448         input_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_244 (BatchN (None, 32, 32, 16)   64          conv2d_254[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_244 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_244[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_256 (Conv2D)             (None, 32, 32, 16)   272         re_lu_244[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_245 (BatchN (None, 32, 32, 16)   64          conv2d_256[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_245 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_245[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_257 (Conv2D)             (None, 32, 32, 16)   2320        re_lu_245[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_246 (BatchN (None, 32, 32, 16)   64          conv2d_257[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_246 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_246[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_258 (Conv2D)             (None, 32, 32, 64)   1088        re_lu_246[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_255 (Conv2D)             (None, 32, 32, 64)   1088        re_lu_244[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_247 (BatchN (None, 32, 32, 64)   256         conv2d_258[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_78 (Add)                    (None, 32, 32, 64)   0           conv2d_255[0][0]                 \n",
            "                                                                 batch_normalization_247[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_247 (ReLU)                (None, 32, 32, 64)   0           add_78[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_259 (Conv2D)             (None, 32, 32, 16)   1040        re_lu_247[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_248 (BatchN (None, 32, 32, 16)   64          conv2d_259[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_248 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_248[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_260 (Conv2D)             (None, 32, 32, 16)   2320        re_lu_248[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_249 (BatchN (None, 32, 32, 16)   64          conv2d_260[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_249 (ReLU)                (None, 32, 32, 16)   0           batch_normalization_249[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_261 (Conv2D)             (None, 32, 32, 64)   1088        re_lu_249[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_250 (BatchN (None, 32, 32, 64)   256         conv2d_261[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_79 (Add)                    (None, 32, 32, 64)   0           batch_normalization_250[0][0]    \n",
            "                                                                 re_lu_247[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_250 (ReLU)                (None, 32, 32, 64)   0           add_79[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_263 (Conv2D)             (None, 32, 32, 64)   4160        re_lu_250[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_251 (BatchN (None, 32, 32, 64)   256         conv2d_263[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_251 (ReLU)                (None, 32, 32, 64)   0           batch_normalization_251[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_264 (Conv2D)             (None, 16, 16, 64)   36928       re_lu_251[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_252 (BatchN (None, 16, 16, 64)   256         conv2d_264[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_252 (ReLU)                (None, 16, 16, 64)   0           batch_normalization_252[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_265 (Conv2D)             (None, 16, 16, 128)  8320        re_lu_252[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_262 (Conv2D)             (None, 16, 16, 128)  8320        re_lu_250[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_253 (BatchN (None, 16, 16, 128)  512         conv2d_265[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_80 (Add)                    (None, 16, 16, 128)  0           conv2d_262[0][0]                 \n",
            "                                                                 batch_normalization_253[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_253 (ReLU)                (None, 16, 16, 128)  0           add_80[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_266 (Conv2D)             (None, 16, 16, 64)   8256        re_lu_253[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_254 (BatchN (None, 16, 16, 64)   256         conv2d_266[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_254 (ReLU)                (None, 16, 16, 64)   0           batch_normalization_254[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_267 (Conv2D)             (None, 16, 16, 64)   36928       re_lu_254[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_255 (BatchN (None, 16, 16, 64)   256         conv2d_267[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_255 (ReLU)                (None, 16, 16, 64)   0           batch_normalization_255[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_268 (Conv2D)             (None, 16, 16, 128)  8320        re_lu_255[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_256 (BatchN (None, 16, 16, 128)  512         conv2d_268[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_81 (Add)                    (None, 16, 16, 128)  0           batch_normalization_256[0][0]    \n",
            "                                                                 re_lu_253[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_256 (ReLU)                (None, 16, 16, 128)  0           add_81[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_270 (Conv2D)             (None, 16, 16, 128)  16512       re_lu_256[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_257 (BatchN (None, 16, 16, 128)  512         conv2d_270[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_257 (ReLU)                (None, 16, 16, 128)  0           batch_normalization_257[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_271 (Conv2D)             (None, 8, 8, 128)    147584      re_lu_257[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_258 (BatchN (None, 8, 8, 128)    512         conv2d_271[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_258 (ReLU)                (None, 8, 8, 128)    0           batch_normalization_258[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_272 (Conv2D)             (None, 8, 8, 256)    33024       re_lu_258[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_269 (Conv2D)             (None, 8, 8, 256)    33024       re_lu_256[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_259 (BatchN (None, 8, 8, 256)    1024        conv2d_272[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_82 (Add)                    (None, 8, 8, 256)    0           conv2d_269[0][0]                 \n",
            "                                                                 batch_normalization_259[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_259 (ReLU)                (None, 8, 8, 256)    0           add_82[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_273 (Conv2D)             (None, 8, 8, 128)    32896       re_lu_259[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_260 (BatchN (None, 8, 8, 128)    512         conv2d_273[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_260 (ReLU)                (None, 8, 8, 128)    0           batch_normalization_260[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_274 (Conv2D)             (None, 8, 8, 128)    147584      re_lu_260[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_261 (BatchN (None, 8, 8, 128)    512         conv2d_274[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_261 (ReLU)                (None, 8, 8, 128)    0           batch_normalization_261[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_275 (Conv2D)             (None, 8, 8, 256)    33024       re_lu_261[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_262 (BatchN (None, 8, 8, 256)    1024        conv2d_275[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_83 (Add)                    (None, 8, 8, 256)    0           batch_normalization_262[0][0]    \n",
            "                                                                 re_lu_259[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_262 (ReLU)                (None, 8, 8, 256)    0           add_83[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_263 (BatchN (None, 8, 8, 256)    1024        re_lu_262[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "re_lu_263 (ReLU)                (None, 8, 8, 256)    0           batch_normalization_263[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_5 (AveragePoo (None, 1, 1, 256)    0           re_lu_263[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "flatten_5 (Flatten)             (None, 256)          0           average_pooling2d_5[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 10)           2570        flatten_5[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 575,114\n",
            "Trainable params: 4,000\n",
            "Non-trainable params: 571,114\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q0pgz6fvajZV",
        "colab_type": "text"
      },
      "source": [
        "- Total params: 575,114\n",
        "- **Trainable params: 4,000**\n",
        "- Non-trainable params: 571,114"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HpzMXab8Us7C",
        "colab_type": "code",
        "outputId": "1008bd96-c84f-4ed5-de59-e2ea2374d21e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"sgd\", metrics=[\"accuracy\"])\n",
        "start = time.time()\n",
        "h = model.fit(train_ds,\n",
        "         validation_data=test_ds,\n",
        "         epochs=75)\n",
        "end = time.time()\n",
        "print(\"Network takes {:.3f} seconds to train\".format(end - start))\n",
        "plot_training(h)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/75\n",
            "391/391 [==============================] - 11s 29ms/step - loss: 2.4399 - accuracy: 0.0947 - val_loss: 2.3885 - val_accuracy: 0.0918\n",
            "Epoch 2/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.3831 - accuracy: 0.0969 - val_loss: 2.3658 - val_accuracy: 0.0910\n",
            "Epoch 3/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.3502 - accuracy: 0.0989 - val_loss: 2.3353 - val_accuracy: 0.0920\n",
            "Epoch 4/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.3248 - accuracy: 0.1044 - val_loss: 2.3130 - val_accuracy: 0.0988\n",
            "Epoch 5/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.3083 - accuracy: 0.1104 - val_loss: 2.2962 - val_accuracy: 0.1140\n",
            "Epoch 6/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.2933 - accuracy: 0.1183 - val_loss: 2.2821 - val_accuracy: 0.1282\n",
            "Epoch 7/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.2798 - accuracy: 0.1268 - val_loss: 2.2709 - val_accuracy: 0.1368\n",
            "Epoch 8/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.2712 - accuracy: 0.1339 - val_loss: 2.2587 - val_accuracy: 0.1494\n",
            "Epoch 9/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.2606 - accuracy: 0.1390 - val_loss: 2.2486 - val_accuracy: 0.1630\n",
            "Epoch 10/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.2535 - accuracy: 0.1469 - val_loss: 2.2385 - val_accuracy: 0.1684\n",
            "Epoch 11/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.2432 - accuracy: 0.1542 - val_loss: 2.2285 - val_accuracy: 0.1763\n",
            "Epoch 12/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.2342 - accuracy: 0.1584 - val_loss: 2.2162 - val_accuracy: 0.1783\n",
            "Epoch 13/75\n",
            "391/391 [==============================] - 11s 27ms/step - loss: 2.2252 - accuracy: 0.1679 - val_loss: 2.2061 - val_accuracy: 0.1836\n",
            "Epoch 14/75\n",
            "391/391 [==============================] - 11s 27ms/step - loss: 2.2159 - accuracy: 0.1735 - val_loss: 2.1950 - val_accuracy: 0.1884\n",
            "Epoch 15/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.2101 - accuracy: 0.1747 - val_loss: 2.1840 - val_accuracy: 0.1915\n",
            "Epoch 16/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.2006 - accuracy: 0.1808 - val_loss: 2.1753 - val_accuracy: 0.1920\n",
            "Epoch 17/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1923 - accuracy: 0.1823 - val_loss: 2.1639 - val_accuracy: 0.1993\n",
            "Epoch 18/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1836 - accuracy: 0.1876 - val_loss: 2.1557 - val_accuracy: 0.2009\n",
            "Epoch 19/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1764 - accuracy: 0.1889 - val_loss: 2.1461 - val_accuracy: 0.2045\n",
            "Epoch 20/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1679 - accuracy: 0.1915 - val_loss: 2.1385 - val_accuracy: 0.2060\n",
            "Epoch 21/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1608 - accuracy: 0.1965 - val_loss: 2.1299 - val_accuracy: 0.2097\n",
            "Epoch 22/75\n",
            "391/391 [==============================] - 11s 27ms/step - loss: 2.1526 - accuracy: 0.2011 - val_loss: 2.1216 - val_accuracy: 0.2128\n",
            "Epoch 23/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1447 - accuracy: 0.2023 - val_loss: 2.1131 - val_accuracy: 0.2139\n",
            "Epoch 24/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1388 - accuracy: 0.2037 - val_loss: 2.1063 - val_accuracy: 0.2156\n",
            "Epoch 25/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1352 - accuracy: 0.2034 - val_loss: 2.0993 - val_accuracy: 0.2175\n",
            "Epoch 26/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1269 - accuracy: 0.2075 - val_loss: 2.0917 - val_accuracy: 0.2244\n",
            "Epoch 27/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1202 - accuracy: 0.2104 - val_loss: 2.0852 - val_accuracy: 0.2267\n",
            "Epoch 28/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1161 - accuracy: 0.2146 - val_loss: 2.0785 - val_accuracy: 0.2295\n",
            "Epoch 29/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1097 - accuracy: 0.2159 - val_loss: 2.0735 - val_accuracy: 0.2301\n",
            "Epoch 30/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1051 - accuracy: 0.2134 - val_loss: 2.0663 - val_accuracy: 0.2322\n",
            "Epoch 31/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1009 - accuracy: 0.2179 - val_loss: 2.0610 - val_accuracy: 0.2366\n",
            "Epoch 32/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0945 - accuracy: 0.2181 - val_loss: 2.0567 - val_accuracy: 0.2399\n",
            "Epoch 33/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0891 - accuracy: 0.2220 - val_loss: 2.0501 - val_accuracy: 0.2452\n",
            "Epoch 34/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0850 - accuracy: 0.2237 - val_loss: 2.0461 - val_accuracy: 0.2455\n",
            "Epoch 35/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0805 - accuracy: 0.2233 - val_loss: 2.0407 - val_accuracy: 0.2502\n",
            "Epoch 36/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0762 - accuracy: 0.2273 - val_loss: 2.0357 - val_accuracy: 0.2503\n",
            "Epoch 37/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0749 - accuracy: 0.2273 - val_loss: 2.0310 - val_accuracy: 0.2520\n",
            "Epoch 38/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0675 - accuracy: 0.2288 - val_loss: 2.0264 - val_accuracy: 0.2560\n",
            "Epoch 39/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0645 - accuracy: 0.2276 - val_loss: 2.0225 - val_accuracy: 0.2576\n",
            "Epoch 40/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0620 - accuracy: 0.2312 - val_loss: 2.0181 - val_accuracy: 0.2580\n",
            "Epoch 41/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0582 - accuracy: 0.2338 - val_loss: 2.0148 - val_accuracy: 0.2600\n",
            "Epoch 42/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0552 - accuracy: 0.2341 - val_loss: 2.0100 - val_accuracy: 0.2609\n",
            "Epoch 43/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0526 - accuracy: 0.2339 - val_loss: 2.0065 - val_accuracy: 0.2600\n",
            "Epoch 44/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0486 - accuracy: 0.2355 - val_loss: 2.0030 - val_accuracy: 0.2621\n",
            "Epoch 45/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0438 - accuracy: 0.2377 - val_loss: 1.9988 - val_accuracy: 0.2621\n",
            "Epoch 46/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0419 - accuracy: 0.2370 - val_loss: 1.9956 - val_accuracy: 0.2622\n",
            "Epoch 47/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0379 - accuracy: 0.2391 - val_loss: 1.9917 - val_accuracy: 0.2625\n",
            "Epoch 48/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0356 - accuracy: 0.2393 - val_loss: 1.9874 - val_accuracy: 0.2649\n",
            "Epoch 49/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0336 - accuracy: 0.2446 - val_loss: 1.9839 - val_accuracy: 0.2654\n",
            "Epoch 50/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0293 - accuracy: 0.2445 - val_loss: 1.9813 - val_accuracy: 0.2671\n",
            "Epoch 51/75\n",
            "391/391 [==============================] - 11s 27ms/step - loss: 2.0261 - accuracy: 0.2453 - val_loss: 1.9785 - val_accuracy: 0.2685\n",
            "Epoch 52/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0230 - accuracy: 0.2431 - val_loss: 1.9739 - val_accuracy: 0.2701\n",
            "Epoch 53/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0206 - accuracy: 0.2441 - val_loss: 1.9704 - val_accuracy: 0.2714\n",
            "Epoch 54/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0179 - accuracy: 0.2436 - val_loss: 1.9674 - val_accuracy: 0.2714\n",
            "Epoch 55/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0161 - accuracy: 0.2463 - val_loss: 1.9658 - val_accuracy: 0.2726\n",
            "Epoch 56/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0159 - accuracy: 0.2478 - val_loss: 1.9623 - val_accuracy: 0.2746\n",
            "Epoch 57/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0108 - accuracy: 0.2486 - val_loss: 1.9601 - val_accuracy: 0.2750\n",
            "Epoch 58/75\n",
            "391/391 [==============================] - 11s 27ms/step - loss: 2.0088 - accuracy: 0.2484 - val_loss: 1.9560 - val_accuracy: 0.2744\n",
            "Epoch 59/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0067 - accuracy: 0.2522 - val_loss: 1.9535 - val_accuracy: 0.2763\n",
            "Epoch 60/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0038 - accuracy: 0.2524 - val_loss: 1.9498 - val_accuracy: 0.2773\n",
            "Epoch 61/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9999 - accuracy: 0.2535 - val_loss: 1.9480 - val_accuracy: 0.2777\n",
            "Epoch 62/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9979 - accuracy: 0.2541 - val_loss: 1.9458 - val_accuracy: 0.2775\n",
            "Epoch 63/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9963 - accuracy: 0.2550 - val_loss: 1.9420 - val_accuracy: 0.2799\n",
            "Epoch 64/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9930 - accuracy: 0.2546 - val_loss: 1.9392 - val_accuracy: 0.2813\n",
            "Epoch 65/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9931 - accuracy: 0.2545 - val_loss: 1.9373 - val_accuracy: 0.2819\n",
            "Epoch 66/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9905 - accuracy: 0.2540 - val_loss: 1.9337 - val_accuracy: 0.2834\n",
            "Epoch 67/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9874 - accuracy: 0.2567 - val_loss: 1.9320 - val_accuracy: 0.2837\n",
            "Epoch 68/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9875 - accuracy: 0.2568 - val_loss: 1.9291 - val_accuracy: 0.2822\n",
            "Epoch 69/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9838 - accuracy: 0.2577 - val_loss: 1.9270 - val_accuracy: 0.2852\n",
            "Epoch 70/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9807 - accuracy: 0.2598 - val_loss: 1.9247 - val_accuracy: 0.2856\n",
            "Epoch 71/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9812 - accuracy: 0.2627 - val_loss: 1.9235 - val_accuracy: 0.2862\n",
            "Epoch 72/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9769 - accuracy: 0.2614 - val_loss: 1.9201 - val_accuracy: 0.2905\n",
            "Epoch 73/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9747 - accuracy: 0.2592 - val_loss: 1.9194 - val_accuracy: 0.2886\n",
            "Epoch 74/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9720 - accuracy: 0.2644 - val_loss: 1.9172 - val_accuracy: 0.2888\n",
            "Epoch 75/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9720 - accuracy: 0.2634 - val_loss: 1.9134 - val_accuracy: 0.2910\n",
            "Network takes 821.904 seconds to train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PXol7EltH0s",
        "colab_type": "code",
        "outputId": "8469361f-d297-4dff-d6f8-958ad64b7909",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "model.save(\"resnet_bn_only\")\n",
        "!gsutil -m cp -r resnet_bn_only gs://batch_norm"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: resnet_bn_only/assets\n",
            "Copying file://resnet_bn_only/saved_model.pb [Content-Type=application/octet-stream]...\n",
            "Copying file://resnet_bn_only/variables/variables.data-00000-of-00002 [Content-Type=application/octet-stream]...\n",
            "Copying file://resnet_bn_only/variables/variables.index [Content-Type=application/octet-stream]...\n",
            "Copying file://resnet_bn_only/variables/variables.data-00001-of-00002 [Content-Type=application/octet-stream]...\n",
            "/ [4/4 files][  3.9 MiB/  3.9 MiB] 100% Done                                    \n",
            "Operation completed over 4 objects/3.9 MiB.                                      \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-kcR7X1b3CO",
        "colab_type": "code",
        "outputId": "3df3f096-ecb8-4fce-d66b-9064dd3590ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = get_training_model()\n",
        "\n",
        "for layer in model.layers:\n",
        "    if not isinstance(layer, tf.keras.layers.BatchNormalization):\n",
        "        if hasattr(layer, \"trainable\"):\n",
        "            layer.trainable=False\n",
        "\n",
        "# Train model with a decay schedule\n",
        "first_decay_steps = 1000\n",
        "lr_decayed_fn = (\n",
        "  tf.keras.experimental.CosineDecayRestarts(\n",
        "      initial_learning_rate=0.1,\n",
        "      first_decay_steps=first_decay_steps))\n",
        "\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=tf.keras.optimizers.SGD(lr_decayed_fn), metrics=[\"accuracy\"])\n",
        "\n",
        "start = time.time()\n",
        "h = model.fit(train_ds,\n",
        "         validation_data=test_ds,\n",
        "         epochs=75)\n",
        "end = time.time()\n",
        "print(\"Network takes {:.3f} seconds to train\".format(end - start))\n",
        "plot_training(h)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/75\n",
            "391/391 [==============================] - 11s 29ms/step - loss: 2.3025 - accuracy: 0.1418 - val_loss: 2.2242 - val_accuracy: 0.1614\n",
            "Epoch 2/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.2114 - accuracy: 0.1684 - val_loss: 2.1709 - val_accuracy: 0.1908\n",
            "Epoch 3/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1917 - accuracy: 0.1823 - val_loss: 2.1309 - val_accuracy: 0.2084\n",
            "Epoch 4/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1420 - accuracy: 0.2024 - val_loss: 2.0706 - val_accuracy: 0.2319\n",
            "Epoch 5/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.1047 - accuracy: 0.2161 - val_loss: 2.0431 - val_accuracy: 0.2391\n",
            "Epoch 6/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0832 - accuracy: 0.2194 - val_loss: 2.0293 - val_accuracy: 0.2456\n",
            "Epoch 7/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0745 - accuracy: 0.2237 - val_loss: 2.0248 - val_accuracy: 0.2473\n",
            "Epoch 8/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0690 - accuracy: 0.2301 - val_loss: 2.0116 - val_accuracy: 0.2474\n",
            "Epoch 9/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0457 - accuracy: 0.2337 - val_loss: 1.9782 - val_accuracy: 0.2620\n",
            "Epoch 10/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 2.0225 - accuracy: 0.2429 - val_loss: 1.9538 - val_accuracy: 0.2739\n",
            "Epoch 11/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9981 - accuracy: 0.2574 - val_loss: 1.9299 - val_accuracy: 0.2793\n",
            "Epoch 12/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9818 - accuracy: 0.2572 - val_loss: 1.9119 - val_accuracy: 0.2837\n",
            "Epoch 13/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9667 - accuracy: 0.2631 - val_loss: 1.9003 - val_accuracy: 0.2896\n",
            "Epoch 14/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9554 - accuracy: 0.2674 - val_loss: 1.8914 - val_accuracy: 0.2965\n",
            "Epoch 15/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9495 - accuracy: 0.2714 - val_loss: 1.8841 - val_accuracy: 0.2977\n",
            "Epoch 16/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9466 - accuracy: 0.2701 - val_loss: 1.8812 - val_accuracy: 0.2972\n",
            "Epoch 17/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9456 - accuracy: 0.2695 - val_loss: 1.8805 - val_accuracy: 0.3000\n",
            "Epoch 18/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9438 - accuracy: 0.2719 - val_loss: 1.8783 - val_accuracy: 0.2981\n",
            "Epoch 19/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9338 - accuracy: 0.2752 - val_loss: 1.8588 - val_accuracy: 0.3028\n",
            "Epoch 20/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9166 - accuracy: 0.2801 - val_loss: 1.8382 - val_accuracy: 0.3095\n",
            "Epoch 21/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.9032 - accuracy: 0.2813 - val_loss: 1.8247 - val_accuracy: 0.3163\n",
            "Epoch 22/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8912 - accuracy: 0.2912 - val_loss: 1.8124 - val_accuracy: 0.3151\n",
            "Epoch 23/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8784 - accuracy: 0.2946 - val_loss: 1.8078 - val_accuracy: 0.3226\n",
            "Epoch 24/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8725 - accuracy: 0.2933 - val_loss: 1.7956 - val_accuracy: 0.3240\n",
            "Epoch 25/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8623 - accuracy: 0.2969 - val_loss: 1.7841 - val_accuracy: 0.3256\n",
            "Epoch 26/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8556 - accuracy: 0.2998 - val_loss: 1.7767 - val_accuracy: 0.3254\n",
            "Epoch 27/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8501 - accuracy: 0.3012 - val_loss: 1.7732 - val_accuracy: 0.3303\n",
            "Epoch 28/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8449 - accuracy: 0.3049 - val_loss: 1.7679 - val_accuracy: 0.3350\n",
            "Epoch 29/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8380 - accuracy: 0.3064 - val_loss: 1.7642 - val_accuracy: 0.3365\n",
            "Epoch 30/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8378 - accuracy: 0.3056 - val_loss: 1.7609 - val_accuracy: 0.3357\n",
            "Epoch 31/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8341 - accuracy: 0.3091 - val_loss: 1.7593 - val_accuracy: 0.3416\n",
            "Epoch 32/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8313 - accuracy: 0.3113 - val_loss: 1.7573 - val_accuracy: 0.3395\n",
            "Epoch 33/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8312 - accuracy: 0.3099 - val_loss: 1.7562 - val_accuracy: 0.3424\n",
            "Epoch 34/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8254 - accuracy: 0.3133 - val_loss: 1.7550 - val_accuracy: 0.3390\n",
            "Epoch 35/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8286 - accuracy: 0.3100 - val_loss: 1.7542 - val_accuracy: 0.3415\n",
            "Epoch 36/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8269 - accuracy: 0.3108 - val_loss: 1.7541 - val_accuracy: 0.3393\n",
            "Epoch 37/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8249 - accuracy: 0.3131 - val_loss: 1.7540 - val_accuracy: 0.3409\n",
            "Epoch 38/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8296 - accuracy: 0.3066 - val_loss: 1.7540 - val_accuracy: 0.3405\n",
            "Epoch 39/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8271 - accuracy: 0.3111 - val_loss: 1.7687 - val_accuracy: 0.3365\n",
            "Epoch 40/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8243 - accuracy: 0.3129 - val_loss: 1.7451 - val_accuracy: 0.3471\n",
            "Epoch 41/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8158 - accuracy: 0.3160 - val_loss: 1.7424 - val_accuracy: 0.3449\n",
            "Epoch 42/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8122 - accuracy: 0.3174 - val_loss: 1.7416 - val_accuracy: 0.3437\n",
            "Epoch 43/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8047 - accuracy: 0.3213 - val_loss: 1.7356 - val_accuracy: 0.3549\n",
            "Epoch 44/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.8011 - accuracy: 0.3235 - val_loss: 1.7319 - val_accuracy: 0.3511\n",
            "Epoch 45/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7950 - accuracy: 0.3242 - val_loss: 1.7252 - val_accuracy: 0.3556\n",
            "Epoch 46/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7901 - accuracy: 0.3272 - val_loss: 1.7196 - val_accuracy: 0.3576\n",
            "Epoch 47/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7887 - accuracy: 0.3267 - val_loss: 1.7146 - val_accuracy: 0.3593\n",
            "Epoch 48/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7830 - accuracy: 0.3311 - val_loss: 1.7144 - val_accuracy: 0.3607\n",
            "Epoch 49/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7804 - accuracy: 0.3319 - val_loss: 1.7148 - val_accuracy: 0.3566\n",
            "Epoch 50/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7739 - accuracy: 0.3350 - val_loss: 1.7059 - val_accuracy: 0.3625\n",
            "Epoch 51/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7748 - accuracy: 0.3341 - val_loss: 1.7029 - val_accuracy: 0.3646\n",
            "Epoch 52/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7671 - accuracy: 0.3345 - val_loss: 1.7110 - val_accuracy: 0.3561\n",
            "Epoch 53/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7648 - accuracy: 0.3378 - val_loss: 1.6964 - val_accuracy: 0.3680\n",
            "Epoch 54/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7607 - accuracy: 0.3416 - val_loss: 1.6997 - val_accuracy: 0.3647\n",
            "Epoch 55/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7611 - accuracy: 0.3400 - val_loss: 1.6859 - val_accuracy: 0.3715\n",
            "Epoch 56/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7570 - accuracy: 0.3416 - val_loss: 1.6912 - val_accuracy: 0.3648\n",
            "Epoch 57/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7522 - accuracy: 0.3431 - val_loss: 1.6847 - val_accuracy: 0.3693\n",
            "Epoch 58/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7508 - accuracy: 0.3441 - val_loss: 1.6819 - val_accuracy: 0.3721\n",
            "Epoch 59/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7473 - accuracy: 0.3473 - val_loss: 1.6771 - val_accuracy: 0.3755\n",
            "Epoch 60/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7468 - accuracy: 0.3457 - val_loss: 1.6743 - val_accuracy: 0.3724\n",
            "Epoch 61/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7477 - accuracy: 0.3458 - val_loss: 1.6744 - val_accuracy: 0.3762\n",
            "Epoch 62/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7404 - accuracy: 0.3490 - val_loss: 1.6724 - val_accuracy: 0.3739\n",
            "Epoch 63/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7432 - accuracy: 0.3461 - val_loss: 1.6737 - val_accuracy: 0.3728\n",
            "Epoch 64/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7381 - accuracy: 0.3472 - val_loss: 1.6709 - val_accuracy: 0.3736\n",
            "Epoch 65/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7366 - accuracy: 0.3519 - val_loss: 1.6743 - val_accuracy: 0.3692\n",
            "Epoch 66/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7381 - accuracy: 0.3486 - val_loss: 1.6638 - val_accuracy: 0.3787\n",
            "Epoch 67/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7365 - accuracy: 0.3520 - val_loss: 1.6681 - val_accuracy: 0.3762\n",
            "Epoch 68/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7340 - accuracy: 0.3497 - val_loss: 1.6646 - val_accuracy: 0.3774\n",
            "Epoch 69/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7322 - accuracy: 0.3512 - val_loss: 1.6654 - val_accuracy: 0.3746\n",
            "Epoch 70/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7330 - accuracy: 0.3521 - val_loss: 1.6650 - val_accuracy: 0.3774\n",
            "Epoch 71/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7361 - accuracy: 0.3504 - val_loss: 1.6630 - val_accuracy: 0.3760\n",
            "Epoch 72/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7307 - accuracy: 0.3545 - val_loss: 1.6635 - val_accuracy: 0.3759\n",
            "Epoch 73/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7270 - accuracy: 0.3536 - val_loss: 1.6622 - val_accuracy: 0.3755\n",
            "Epoch 74/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7328 - accuracy: 0.3496 - val_loss: 1.6623 - val_accuracy: 0.3772\n",
            "Epoch 75/75\n",
            "391/391 [==============================] - 11s 28ms/step - loss: 1.7308 - accuracy: 0.3543 - val_loss: 1.6610 - val_accuracy: 0.3776\n",
            "Network takes 831.964 seconds to train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JvaImTpxIREQvvvgiAag2X793715as2YNEQlRAcdxtHXr1irlli1bRkeOHKlRLHiepylTppBMJot6Hnbv3k0A6MUXX6RbbrmFAKFTx/liGBGL2bNni+k0IqJff/2VAND9999PRET9+/ev0j4XYfny5VUiyt9//11sR2npMLFowkTC4lmzZtVYJpLL3rRpE5WWlopO+5577olyVDNmzBDz3+cSGZ/w1ltvib2jWrduTeFwuFqx8Hq9dMMNN1DXrl3FFNSxY8dEp7Vhw4ZqxYKIaMGCBWQ0GmvMl0cEI5J3Tk1NpT179sR1ryKOtXPnzlEptsg2btw4OnLkCEmlUrr11lvp+PHjdPToUfrggw/oscceI41GQ23bto2Zyw8GgzRu3DgCQGvXrq22TH5+PqWmplLr1q3jGlgX6fl06tSpqP2lpaWk0WjorrvuqlYsiARHnZiYSD///HPU/oyMjGrF4qabbqq1x9L5TJw4kZKSkmjp0qW0evVqeumll2jt2rXUq1cvSkhIICKhU0WPHj1IJpPRG2+8IbZrrVmzhrKzs+nZZ58lIiGtqNfrKScnh1atWkXBYJBCoRC9/PLLpFarafPmzWIX4vPHqUTaMh5//PEqNkqlUrGdKCIYvXr1ihKM7777jgDQ4cOHo861Wq1kMBjEtOOOHTvIaDRSVlYWrVixQrTx1VdfJY1GU+WZPnnyJEkkEnrxxRfjvqfNFSYWTZiVK1cSgFoHLL322muUlJQk9m45VzDuu+8+sdzAgQOrDa95nqf09HR66623xJA+kn+uTiyq48iRI6JT9vv9NYpFPOzatYvGjBlDU6ZMqXV07fnk5+eTQqEgmUxGo0ePpkWLFkUNZIxEU7Nnz47qVaTT6ejGG2+stvdLOBym22+/naZMmUKzZs2icePGUa9evchgMIhdhjdv3kzjxo2jV199le69914aO3YsJScnU9euXWvtenku06ZNo549e1Z7bMSIEbWKRU1U12YxZ84cateuXdxRRaSe6hqbn3/+eTIYDOLniooKuummm8RUUKTtKy8vL2pw6C+//CKOgzCbzdSuXTsCQHfddRcRER09epSUSiV17txZTCdt2bKF9Hp9VNozwubNm6OiqPMFI9JTavr06dS5c+dqv+PYsWOj2qi2bdtGXbp0IUDoQhsZz3TbbbdVSXFF/i5/BrGQgdHkkcvlNR4bOHAgBgwYAIPBAABISUnBunXrMH78eMyfPx8PPvggevbsCa/XC6VSWeV8juPw5ptvIi8vD0uXLgUApKam1stOs9kMqVRar3MjdO/eXbSjLuTl5cFmswEA1Gp1jeX++c9/4v7778eSJUvQt29f5OXlQaVSVVuW4zhIpVLs3r0bu3fvxnXXXYfrrrsO48ePh9FoBAAkJCSgtLQUy5cvh8FgwMiRIzFp0iSMGDECEkl8U6/V9LcBgJkzZ+LkyZNx1RPB7XbD6XSC53kQEVasWIGVK1di8+bN+PHHHyGTxfezt1gsOHXqVLW2tWvXDpMmTRI/m81mLF++HDt37sTWrVsBAFOnTsW4ceOizr/qqquwd+9erFixAqdPnwYAdOzYEYMGDQIAtG3bFi+++CKefPJJaLVayGQyuFwuZGdnY+3atVV+C9nZ2Vi2bBl69uwJQPitfPLJJzCZTFiwYAEWLFiAqVOn1nqPn3vuORw4cED83Lt3b+zZswcrVqxAUVERAKBDhw4YMmQIOI6rto54/9bNmsZWK0bNFBYW0l133VWvAWs8z9N///tfcjgcRETkcDjIZrPVes7mzZvptddeE9sTPv30U+rRo0fMazkcDpo1a5aYMrJYLJSZmRnVG4VRM36/nywWS61lDh06RImJiXEN+ou0KyUnJ1NWVhZJpVIaP358nUeW7927l7p06VIlPXaxCYfDNG/ePLr22mupQ4cO9OKLL8bdIH8u3333ndjGEAgEau3aW1/y8/Np8ODBUSPtWyocEVFjCxaj6eJyuaDT6S7ZeYyaifeeLl26FOvXr8ewYcOQlJSE5ORktG3b9hJYyGjJMLFoYhAR7HY7KisroVaroVarUVFRgV9//RWlpaVwOp3w+/0IBAIIBAIIBoPweDxwu93wer0IBAIIhULIzs5Gt27dUFJSgl9//RUJCQliSK9QKCCXyyGTySCXyyGXy5GWlob+/ftDr9fDarVCr9ejX79+UCqVcLlcKC4uhkKhgFarhdForDU11pwJhUKw2WxwuVxwu91wOBzivfV6vfD5fHC5XHA6nfB4POIWCATg9/vh8/kQDAYRCoXEjed5MSUEQExlyOVyaDQaJCQkwGw2w+/3Q6lUQi6XQ6fTwWg0wmg0IjU1FVdffTX0ej2CwSB8Ph90Ol2NKZGmjtPpRGVlJdxut7h5PB44nU44nU5oNBrk5eUhIyMDEokEZ86cwerVq7Fr1y74/X4Eg0EEAgGEw2GxTo7jIJPJoFAooFAoxHsmkUhw/PhxmEwmJCcnQyqVIiEhAQkJCTAYDEhISIDJZGoRz7Pf78eZM2dgtVpRWVmJ0tJS8fn1+Xzis+r3+8VnOvKshsNh8DyPbt264fXXX6+2/hYnFlOnTsX+/fuhVqvFH6Fer4fRaIRarYZOp4PJZILRaITBYIDZbIbZbBbzow0Bz/Pwer1wOp1wOBzweDxwOBxwOBxwuVwoLS1FaWkpSkpKUFFRIR6zWq0oLi6Gz+ertX6O48QfhUKhgFqthlarhVqthlKphFQqhVQqBcdx4DgORASe5xEOhxEKhUSRCYVCCAaDouDYbLao9RfkcjkSExNRUlJSxQaVSoWEhAQkJiZCp9NBq9XCbDYjKSkJBoMBmZmZ0Ov1MJlM0Gq1MBgMMBqN4o9UrVY3uLMLBAIoKytDZWUlnE4nXC4XKioqUFFRAZfLJTp6q9UKh8MBu90Op9MpOiyXy4Xy8vI6rUEREXSFQgGlUgmVSiUKcWSTSCTiFoHneQSDwSgR8ng8otgEAoGo63Ach/T0dJSWliIcDkOhUCAlJQXJyclISUlBeno6UlNT0aVLF3Tp0gVarRaA8PKh1+uh1Wqh0+kaLLdORPD7/eKLSsTh2+12VFRUoLi4GCUlJeK/JSUlqKysFP8W8aBUKqHT6aBWqyGTyaBSqUQxVSgU4jMeuZ+RZzsQCMDn84m/v3jWrdBoNNDpdNDr9eI9TUxMhNlshkajQXJyMpKSksRn3Wg0wmQyicLTEPeViBAIBODxeOByueBwOFBWVgar1Sp+jnwnu90u+ouysjJYLBaUlZXVWr9UKoVGo4FSqRT9xbnPqlQqxRVXXIF58+ZVe36LFIsdO3bA5/OhsrISNpsNTqcz6i2kJuRyOZRKJRQKBTQaDTQajfiARm6mRCIRHW/kRx0MBkVnE/nBx0IqlSIlJQUpKSmimCUkJCAtLQ3p6elISkqC0WgUH0yz2QyTyQSDwQCZTHZR3ip5nhff8Gw2G9xuN2w2G+x2O3w+H3w+H9xut+hkKysrUVlZKb6FV1RUoLKyEg6HI+biPVKpFFqtVhS7iEOIRDoSiUQUvcgPMRwORwlexKZAIACXyxWXE4q8yUfe2vV6PTQaDbRaLfR6vfg30Wq14r7IDyuyRZyKSqW6aA2bwWAQDocDNptNdBJ2ux12ux2lpaWwWCywWCwoLy8XHbLFYkEwGKyxTo7jRKGOOFy5XC4+4xHnK5FIwHGcGBEFAgF4vV7RiUXeSmO5DolEgpSUFGRkZCAtLQ1JSUkwm83IyMhAYmKieN+1Wi00Gg0MBgP0ej10Oh10Ol2Dve2Hw+GolwObzSbeV5vNBqvVKvoJp9Mp3teysjLYbDZ4PJ5a64/cV61WK97XiB+JOONIx49zn2G/3w+/3w+v1ytGs/G4Y5lMJvqL1NRU8d5mZmYiMzMTSUlJMJlMSE1NhdFoFP2YXC6/IL/R4sSiOogIHo8HXq9XfLO02+1wOBwoLy+H1WoV34wiKZ5IyObz+eD3+8VQjYjEXjLn/uAiD3jkLV+j0UCv14vOxmAwwGAwQKfTITk5GYmJic02jRAPHo8HFotFvLcRR3eu83O5XKIjirxRR7aIIEfuOQBRQCLpBpVKBZVKBYVCAZ1OB7PZLL4BRpxOJP2g1WovqnNvCvA8L6YdIqmISGR17v2PpB8iLzqRZzxyryNbRDiUSmWUUEae78izHvkcec4TExNF0W0J95vneZSXl4tRUeR5johM5KXU7XaLz2/kJSYSwUei1XOfYaVSCaVSKb7A6HQ6qFQq0XdE7qXZbIZOpxPF9GJE5REyMjIwatQovPfee1WOtUix6NChAwYMGIAPP/ywsU1hMBiMZkObNm3Qr18/fPzxx1WONX/ZrwaFQoHKysrGNoPBYDCaFRqNpsY2nhYpFmq1mi3GzmAwGHWkNt/ZIsVCoVDEbGBlMBgMRjS1+c4WKRaRHksMBoPBiJ/afGeLFYsW2G7PYDAYF5XafGeLFItIt7/6sOukFRUulsJiMBh/PmrznS1SLHier5dYvP5dPsa8uwUfbSloeKMYDAajiVOb72yRYlHfyGJQXgoAYNHWQngCoYY2i8FgMJo0f7rIIhwO12tdhStbmdA9OwE2TxBf7zpzESxjMBiMpkttvrNFikVk9s66wnEcJvZtBQD49NdC1kjOYDD+VNTmO1ukWPh8vhpXP4vFiK7pSNQqsP+0AzsKrQ1sGYPBYDRdavOdLVIsgsFgvWesVMmleGxwOwBgDd0MBuNPRW2+s0WKRSAQgEKhqN/JZYdxZ58cmDVyrNlfgiJr7dMTMxgMRkuhNt/ZIsWi3pHFb/8F3usH2fb38fF9fcATYdEvhQ1vIIPBYDRB/nSRhdfrhVqtrvuJ6gSADwLfP4vOgX14ZFA7fL79FCzO2IsZMRgMRnOnNt/Z4sQisgBMQkJC3U/uPAq4+nGAwsCSe/F430S0S9FhyBsb8X9rj8DtZ2MvGAxGyySW72xxYhFZmtBoNNavgsHPAdl9AFcJZMsfxId3XQGplMObPxzGgNfX470NxxAIsUkKGQxGyyKW72xxYmGz2QCg/mIhlQFj5wNqM3D0R5h3v4cNfx+IK1uZUO4K4LU1+fjn0n1sDAaDwWhRxPKdLU4sysvLAQCJiYn1ryQhBxj9xxq0a2chofB7LHmoLz6ceCUUMgm+2lmE/2wuuHBjGQwGo4kQy3e2OLGwWoWBdBckFgCQ9xdgwNMA8cCXd4M7+A2u65yGN27pBgB4aeXv+P5AyYWay2AwGE2CWL6zxYlFRB3NZvOFVzbwaeDqqQAfApbcAxz8Fjddnol/XJ8HIuDxL3bjYLHjwq/DYDAYjUws39nixCKSdzOZTBdeGccBQ18QekjxIeDLScCBZXh4UFuMvSITnkAY93y0HQXl7gu/FoPBYDQisXxnixMLj0cYca3VahumQo4Dhs48KxhL7gF3fD1eHdsNPVuZUGz3Yex7W7DrJJtHisFgNF9i+c4WJxalpaWQy+UwGAwNV6koGFOFNowV0yAP+/Dfe3ujf/skVLgDmPDBVqzcW9xw12QwGIxLSCzf2SLFIiUlBRJJA381jgMGPQukXAZYTwA/Pg+tQob/TOqFCb2y4Q/xeOTTnXjnxyMI86xbLYPBaF7E8p0tTiyKi4uRlpZ2cSqXKYAx7wESGfDrB8CxdZBLJXjl5q6YPqITOA5468fDuP6tjfjqtyI2eI/BYDQbYvnOFicWFosF6enpF+8C6ZcLvaQAYNkUwF0BjuNw/7Vt8J+7eyEzQY1jZW488eUeXPPaOry/8RjO2LxsEB+DwWjSxPKdHLUwL5adnY2hQ4di4cKFF+8ifBj4aCRw8hegw3BgwieARFiKMBDi8fXu05j/83EcLnWJp+iVMrRK0qBtsg6tErVI0ilg0ihgVMuRbdagdVIDNcgzGAxGPYjlO2WX2J6LChHBYrEgJSXl4l5IIgVu/gD49zXA4dXAojHAqHlAQjYUMglu7ZmNW67MwobDZfjflgLsOmWDzRPE/tMO7D9d/biM/u2TcF//Nri2fVKNC6YzGAzGxSAe39mixMJutyMQCFx8sQCEKUHGfyKMvTixEXi3L3DdTODKewCJBBzHYVBeCgblpYCIYPUEcaLchaMWF05bvSh3B2DzBGDzBLHrpA0/H6Nx6cMAACAASURBVCnHz0fKcVmGAQ8NaIthl6VBIWtxWUIGg9EEicd3tiixsFgsAIDU1NRLc8HW/YEpW4CV04D8FcDKJ4C9i4Fr/ga0HQzIhIXPOY6DWauAWWvGla2qjo60ugP4bPtJ/GdTAQ6cceCxz3YhSafAbb1zcGvPbGSbNZfm+zAYjD8l8fjOS/rqWlhYiK5duyInJ0c07ny2bNmCQYMGITc3F59++mmd6nc4hBRPvWecrQ/6VGD8x8At/wV0qcCpbcBnE4DX2wFfThbEo+IY4HcCNTQPmbQKPDywHTY9NQgvje6CDqk6lLsC+Ne6oxjw+npM/XwX8kvYtCIMBuPiEI/vvGSRxYoVKzB27FhkZWXh1KlT+PLLL/HII49Elfn+++8xcuRIhEIhXH311Xjsscdw4sQJTJ8+Pa5r2O12AJdYLABhDMZlo4E2A4Ed/wH2LwVK9wEHlgpbBKkC0CQChkwg5yogtz/Qqh+gEgbBqORS3HlVK9zRJwfbC6z4dFshVuwtxvLdZ7B89xkM7ZSC+/u3Qe/WZtauwWAwGox4fOclEQuXy4WxY8diypQpePnll9GzZ0+Ew+Eq5d577z1MmjQJDzzwANq0aYOCggIMHToUXbp0wahRo8RyBw8eRH5+PiQSCZRKJRISEtCuXTtRHfV6/aX4WlVRJwD9pwlb5Qng0Grg6A9AxVHAXQ4EPYCzWNhO7wB+mQtwUiD3GuDGtwFzGwBC2qp3azN6tzbj78PyMP/nE/h8+0n8eNCCHw9acHmWEbf1zsG1HZKRkVCP5WMZDAbjHOLxnZdELLRaLb766isMHToUdrsdhw8fRu/evaPKnDp1CsuXL8euXbtw+eWXAxCmyr366quxc+fOKLFYvHgxZs6cGXX+woULxbftBp3qo76YWwN9Hxa2CAEP4K0Eyg8DBZuE7fRvQgP5e1cDQ54Hej8AnDOCMsukwcybLsMjg9ph0S8FWLS1EHuK7NhTtA8A0DFNj790ScPQTqnonG6ARMIiDgaDUTciYlGb77wkYsFxHG644QYAQP/+/TFlyhRcddVVUWVWrVqF7t27i0IBCBNbHT9+HI8++mhU2eqiEqPRiFOnTgFA/dbfvhQoNMJmzBIawAHAXQGseQrY96Xwb/4K4KZ/CWJzDsl6JaZdn4cpA9th+e7TWJtvwS/HKpBf4kR+iRNv/3gEJo0cfdsmon/7ZAzpmIIUg6oRviSDwWhuRNJQtfnOS9obauvWrdi1axc++uijKsfC4TBksmhzZs2aBZPJhMGDB0ft79SpE0aNGoVwOAy/3w+r1Yr09HTs378fQBOJLOJFmygs49p5NLDicaDgZ+Ddq4ArJwP9HhWE5RzUCikm9M7BhN45CIR4bD5Wju/2l+Cnw2U4Y/dh1b4SrNonLMqUbVbjyhwTrsw1o1umEUl6JZJ0Cihl0sb4pgwGo4kSEYvafOclG8FNRGjTpg0efPBBPP300+L+J598EgDQp08f3Hfffdi0aRMyMzMxe/ZsfPHFF1i7di3atWsX1zWeeuopvP322/D7/RflO1x03OXAmn8C+xYLnzkp0OkGoP8TwjQjtUBEKKjwYMuxcqw9aMHW4xXwBKpGYABg0siRalBBr5JBr5LDoJJBp5JBKZNCJZdAKZNCo5CKx/UqGRLUCrRP1UElZ0LDYLQ04vGdlyyymDFjBgoKCvD7779j7ty5kEqlmDhxIlwuFwYMGICbbroJAwYMQJcuXQAA48aNw6FDh6BSxZ9KCQaDkMvlF+srXHy0ScDYD4Gr/wpsegv4ffnZrdXVQN9HgQ5/iWrTiMBxHFonadE6SYs7+rRCmCfklziws9CKHYVWHC51odLtR7krAKsnCKsnWGfz5FIOV+SYMKRTCq5pl4yOaXrWRsJgtADi8Z2XTCx27NgBANi3bx+2b9+O/Px8dOzYEe+++65Y5uuvv8aGDRsglUrRv3//Ol/D7/fXSVyaLGldgXH/ARzFwJZ/Ab99BBRuFrakPODafwBdbhbno6oOqYTDZRlGXJZhxF19c8X9PE8od/thcfjh9ofg8IXg8Abh8ocQCPHwBcPwh3i4AyE4fSE4fUE4fSGUOf04WubCthOV2HaiEkA+2iRrce81rXFzjyyoFSziYDCaK/H4zkuWhvL7/XC73TCbzcL0F1Zrw6yTfQ6TJk3CunXrcPLkyQatt9HxOYBdi4Bf3gUcRcI+TaLQSN7hL0D768WxGhcTuyeIn46UYX2+BT8fLUeZUwhZjWo5bu+Tg7uuasW68jIYzZB4fGeLmnV2woQJ2LlzJw4fPtzYplwcwkFgz+fA5neAiiNn90uVQO7VQGZPoW0jvRtgzBYGC14kgmEeq/YV4z+bC7DnlE3cf0VOAkZ0TcfIbulINzLhYDCaA/H4zhYlFqNHj8aJEyewZ8+exjbl4kIElB8Bjv4IHPxWmCod5/0ZlQbAlAukdAZSOgn/mtsIEyDKFA1qzs6TVizYdAI//l4K/zkLPvXKNWF4l3QM75rGhIPBaMLE4ztblFhcf/31cDgc2Lp1a2ObcmlxlgqCcWYnULwXKN4jDP6rCV0qYMgA5BpAoQWUemHjpMIqgFK5MAmiUg+oTYAqQWh8z+olTo5YHW5/COvyLVi1rxhr8y3iSoEcB/RqZcb1l6VieNd0ZLJUFYPRpIjHd7YosRg0aBBCoRB+/vnnxjalcSECPJXCNCOW34WtLB+oLBDaPKiey72qzcAVE4E+DwGG2lcjdPqCWJdvwZr9JVh70IJA+Ow1e+eaMfbKTAzKYwMHGYymQDy+s8WJBc/z2LhxY2Ob0nQJhwDnGSEaCfmAgBvwO/6YFZcH+JDQNhLyA3474LUCXhtQeVwQHQCQyIHutwHXTKsy0rw6HL4gNh4qw+r9xViXb4EveFY4UvRKdM9OwOXZCbgsw4A+rRNZzyoG4xITj+9sUWIxcOBAEBETi4sBEVC0A/jlX8Dv3wAgQTR6ThZEI0akEcHlD2HFnjNYvb8EOwutcPpDUceVMgn6tU3EgA7J6N8hGbmJWkjZWA4G46ISj+9kYsGoO+VHgZ/fEHpmgQCZGuh9P9DvMUAX/yqFPE8oqHDjt0Irfi92YEeBFftO26PKaBVSdMk0omOaHnlpBrRL0aF1krCGOZumncFoGP50YsHaLC4xpb8DG2YLPbIAoYG83RDg8tuADsOExvM6YnH4sPFwGTYcKsOOwkqUOqqffkCrkKJVohY5Zg2yTGpkmzXITFCjTbIW2WYN5NKLv64XzxMq3AGEeB5EAE8EnhcG2Es4ToyICsrdOF7uRmGFBycr3eB5QCmXQCGVQCGTQKOQQqOQQasU/k3RK9ExzYAsk5qNkGdcEv50bRZDhgyBz+fD5s2bG9uUPxendwI/vQEc+U5o8wCEaKPdEKDTjUDecEBVvwWpLE4fDpx24FCpE4dLnDhW5kJBhQd2b83TlcgkHDJNarRK1EIh5RAMEwIhHsEwLzppqYQDx3HgIIx2l0o4EAHeYBieQAi+oDCaPcQTDCoZTBoFEjQKaBRSlDh8OG31oqDCXeP8Ww2BViFFx3QDOqTqkaxTIFmvRJpRjWyzGm2SdGyNdkaDEY/vbFFicd1118HlcuGXX35pbFP+nLjLgb1fAAeWAUXbz+6XKoXR5m0HC1OZJLYTuuJeQBrJ6g6goMKNU1YviqwenKoU/j1e5sZpm7cBvkx8JGjkUMok4MBBwgGSP0QnzBPCRCAiZCao0TZFh1ZmLXKThKjHHwojEOLhD/HwBMLC5g/BHQihyOpFfolTHCFfHQqpBJ3S9eiaZUTHNAMyTWrkJmqRmaBmIsKoM/H4zhYlFsOHD0d5eTm2b98euzDj4mIvElYKPPC1MKdVdYMG0y8Huo0HOo4ENA039YsvGMbJSg9OVXoQ5glyqQTyP1I+vmAYwTCPME8gCLP1hnkgxPOQcBzUcmHGXaVcCrVcCqmEg90bhM0TgM0ThDsQQqpBhYwENVqZNTBpG3aA47lUuPz4vdiB42VuVLgDKHP6cMbmQ0GFGycrPdUu6S6VcMgyqdE+RYcumUZ0Tjega5YRaQYVa+Nh1Eg8vrNFicWNN96IoqIi7Nq1q7FNYZyLswQ4vAY4uQ0oOwhUHBe65UbgpML65V1uBjrddEnmuWruOH1B7CuyY/8ZO46UunDa5kVhhQdn7N5qRSRFr0TnDAO6ZRqRl2ZAm2QtMk1qGFTNeJZmRoMRj+9sUWIxZswYHD16FPv27WtsUxi1QSSkrA6tElJWJ34C6I/cv0QurEnecaQwSaIx66LOcdXoeG1ClFXNtPP1wR8Ko7DCg/wSJ/aesuFgiQP7iuxw+ELVlk/QyJGiVyLHrEGXTCO6ZBjRJdOINCMbLPlnIh7fGZdYuFwuzJ07F2PGjEFeXl6DGtmQ3Hrrrdi7dy/y8/Mb2xRGXXBXAPnfAvuWCCmrc0eY6zOAnD5ARg9hM2YJ++TN2Jk5ioUFrvYtAUr2Ct+n8yhhvfaEnAa/HM8TCis9OHDGjn2n7ThmceF4mRvFdh+8weob6PUqGTKMaqQnqJD+R6N6XqoeHVL1yExgvbRaGvH4zrjEwu/344477sCyZctw33334eWXX0ZSUlKDGtsQTJw4ET/99BMKCgoa2xRGfXFXCL2q8lcKS8z67NWX0yQJAwETWgkTJJpaAQm5gD5NGOuhSax1vY96Ew4BQY8wyj0cAMJ+wUZnKRBwCiPfA24g4AKCPuF4yA8EvcIoeVdptCBy0uio6oqJwNVTAW0yIFdf1KiKiFDm8qPM6cfxMjf2nLLh92IH9p22w1lDJAIAGoUUKXplVGO+Si5BXpoBHVJ0aJeiQ5ZJA6VcAgn3R8M/x0GnkkGrkEEll4jtJ4EQD08gBLs3KNRFBA4AT0KvNqmEg0ourOAY6aHGE4EnQMIJywxHuiBLJRy8gTC8wTB8QR7+kNBxwBsIw/XH4M/MBDVyEjUs/XYe8fjOOqWhNmzYgGeffRa7d+/GqFGj0Lt3bwCATCbDpEmToNXWvV99Q3L//fdjxYoVKC4ublQ7GA0EzwPlh4SR46d/A0oPAM5iYeNrdmYCnDAJojpB6Lar0AkTI0pkggOOOOlQAAi6BedPJDjxgFtw7Hzw7BQoQa8wPUp959U6F4lM6E7cbQLQbihgOQBsfU+INM7tCMBJAIVeEI3IBI9ShdCmk9hemE04qQOQnCdMSS9tmLXMiAhWTxDFdi9OW70ocfhwvMyNw6VOHC51odx1YcsWy6UcVDIp/GFenGzyUpOsV6J1ohb+MA+3PwSPP4QgT0jSKdEmSYv2qYLopRvVSNErkaxXtuglhePxnXVus1i6dCnGjh0LALjssstw4MABAMBLL72E6dOnX4C5F84jjzyCzz//HBUVFY1qB+Miw4cBlwVwnAGsJ4DKE4CtELCdFPa7LcJEiuf3wGoQOGG2XplCcNxShSBGuhRBjGRqQPHHbL4ytTBLr0wlpM2UBmF/Zk9An1q16rJDwIZXhF5kREJUErdZEkCbIswmbG4DpHURrpPWVRDMBsTmEZbmDfM8OI6DlOPg8AWRX+zEEYsTRy0ulDj8CIZ58DyBJ6EbscsXgjsQjhIIqYSDRi6FQS0/Z7wLQcJxCPGEME/wBELwh3goZRJoFDJIJICU4xAmgjfAIxAKIxDmwfOASi6BWiGFSi6FUiaBWi6FWiGFTikDT8BpqxeFle6o+cniRfNHPQqZBDIJB6VMCqNajmS9EmatAok6BTIS1GiTJAwMTdErm00PtHh8Z1yvIjzPY+/evXjhhRewevVqPPzww3j22WeRlpYGm80GIoLRWL9BVw2JRqOB13vp+tgzGgmJVEhBGdKBrCurLxMOCZMg+mzCSoN+h5Aa4sNCdEC84GBlSuHNXaoUPnOc8FlpEISAkwhv9XKV4Pwl0ouXGkrOA275SBAKjhOinYBLiGoiEzyGA4CnQhCWsnzh3/IjQrTlKhG2MzuB/UvO1pvYDki9DEjrJkwzn375BQlIwh8DFM+nW1Z8dfpDQppIKZMIY1QusUPlecJpmxcnKz1QyaXQq2TQKKSQSSQocfhwzOLCYYsTxyxulDl9KHX4UeH2i+Nh4kUhlSBRp0CqQYUcswZmrQIJGjkUMgn0KjlUMgm0SpkgpjzBGxCWNM4yqdEmWRdzzEwkAnR4gwidI9xymaTOywDE4ztjioXH48ENN9yADRs24KqrrsKvv/6Kbt26icdNJlOdjLqYRL4wz/OQNFDvEkYzRSoDdMnC1tyIOE+p/I9UWjW/sdxroj+Hg0J7iL0IKD8srGlStAOwHBSmqq84Cvy+/Gx5c1sh6si8UhAPcxthoKRMddF7nyllUihljZfSkUg4ZJs1yDZrqhxLM6rQPbuq6BERnP4Q3H+sVR/ihVkBrJ4Aypx+VLoDKHf5cbLSi5MVbhRWemDzBFFs96HY7sPuc1aTjNtODkg3qpFqUCJJJ0QvepUM5a4ACivcOGpxVdvLLVGrwG/PXVena8XjO2OKRVFREdLT07Fz50507969TgZcajQa4Y/v8/nE/zMYfwqkcqGnmDELyLnq7P5wUGjrsRwEzuwSRtZbfgcqjwnb719H18NJzi54pTIKEYhCK3QkaDcEyOoNKHWx7YlMktVC4DgOBpW8Tg3jvmAY5S4/iqxenLF5UekOwOENwh/i4fCF4A8KkYpEItSvkUshk0pwstKNgnKP0GZk89Y6I4FeKYNJq4BMwoEgdDZI0NS98T4e3xlTLDp06IBPPvmkyn4iwurVq3HddddBLm8aPQv0ej0AwOl0MrFgMABBRDK6C1v324R94aAgGCX7gaJfhQkhbYVC2i6S5vJUk7v+Za4gJimdgeSOQjuNLkVYeZF4IR1m+V2oz1UCtB8mzA3WbujZCC/gFgZpusuEelrwAEyVXIoskwZZpvr5okCIx2mbFxaHDxXuAKyeABzeEMxaOXLMWrRN1iK5gdpF4vGdMcXC5XLh888/x6RJkyCTRRd/88038fTTT2Pbtm1Qqxt/qUydTnjjcblcSE2tpgGRwWAIApJ+ubD1uCP6WDgodA7w2YX2Hq9NaDcp3i0MnizZB5TuF7ZYHFopbOCAhGzA74pe7lciB7J6CqP3c64ShKT0gJBCO7NLaKeRa4RIRmkQ2pf4sNCGAwgdCVRG4ZjaJHQwACd8P4kMAAnXFHu78cLxSOcEifxs1+ZwQKhbZRA6CmgShU4IEplwLOQXjie2E4TyEkRNCpkErZO0aJ108XuZxuM7Y4rFlClTUFBQgLvvvjtqP8dx+Oyzz3D55Zdj6dKluOOOO2qo4dKhUgkDtVgjN4NRT6RywUme31ury83Cv0EvcGa3EIm4LEI7icsCgIR2kJROQmO6TCWM0D/8HVCwSeipBghOWp8GyLVCA/3JX4StOaFNATpcD3QYDiS2PSsmQY/wvY3ZgD69WaXh4vGdMcViz549aNOmTbWppuTkZFx//fVwuVwXYGbDEYlumFgwGBcJuRpo1VfYYtHnQWELeITeWgqdMNgw4kS9NkFITmwUIgmVUXhrT+smpM2U+j8GMzqECCHkE3qjyf9IkwRcQh1+xx8RkPuPcTFBIQoAJ0QlcvUfPdukwvFwQIg0+KCwX6b64zgnRFTucsBTLqTLiAQBlSmF+op3C99l18fCVhMKnTAGxpglRCsSuWCLJlE4Fhn7o00S0niGzAYbJ1Mf4vGdMa275ZZbMHPmTKxYsQI33HBD1DGfz4fvvvsOQ4YMuUBTGwYmFgxGE0ShEd7Az0edAHS6QdiaC0RCquzwauDoWqHtRaYWUltyjRBdWAsFsTmzU9jiITJORpcc3WVbIhPETqET7qNMBYATBpTyobNdwcEJ4hfyCx0Sxvy7Tl+rQcRi6tSpWLlyJW6++WbMnz8fd911FziOg91ux1NPPQUiEgfpNTaREeRut7uRLWEwGC0SjhMGPKZ1Aa79R83l3OVCd2XHaSEqCgf+iICswme/U9jcZUIEc+44mQtFk1jnU+LxnTHFwmAw4LvvvsOIESNw991344033sDw4cOxfPlyFBYWYvny5U2m55HBIPSscDqdjWwJg8H4U6NNErZ4CQfP9hLjw+dEDn9MNRNpqA8FANA5kYdUSK+BhM8ylRCF1JF4fGdcSTKj0YhNmzbh7bffxrx587B48WL06NEDX375Jbp27Vpnwy4WLLJgMBjNEqlc6DGWkN0ol4/Hd8bdXM9xHP72t7/h6NGjOHHiBP7zn/+gvLwc69evR0lJA4RODUCk+xcTCwaDwYifeHxn3M3vv/32GxYvXozCwkJs2rQJpaWlCIVCSEtLwzvvvINbb731wi2+QBISEiCRSGCxWBrbFAaDwWg2xOM7Y4pFSUkJpkyZgq+//hodO3ZEcnIyTp8+jaeeegqjR49Gnz59mszMijKZDElJSUwsGAwGow7E4ztrFYvTp0+jW7du0Ol0WLZsGYYOHYpVq1bh559/xp133okuXbo0uNEXik6nYw3cDAaDUUdi+c5a2yxKSkpQWVmJJ598EqNHj4ZOp8Po0aORkZGBJUuW1HZqo6HValmbBYPBYNSRWL6z1sjiiiuuwFNPPYUXXngBBw4cQKdOnXDNNdcgHA5j48aNDW5sQ6DVauHxeBrbDAaDwWhWxPKdtYoFx3F45ZVXkJubi48//hjvv/8+eF5YYaq0tBTt27fHoEGDkJycjJdeeqlJtF3o9XqWhmIwGIw6Est3xmzg5jgODz30EB566CG4XC5s2rQJZWVl4vFDhw7FPTfU//73P0ilUkyYMAFS6cVZ/MRoNKKoqOii1M1gMBgtlVi+M+6us5WVlVi8eDHGjBlT7+m/H3vsMTgcDjidTjz00ENRx44ePYr8/HwAgN1ux/r16/HDDz/g7rvvxj//+c+4p0A3GAyw2+31so/BYDD+rMTynXGJxfHjxzFw4ECcOnUKixYtwubNm8Vj+/btwz/+8Q+89dZb6NSpU631TJ48Ge+88w7++c9/4p577oFCcXYd37vuugtbt24VP6ekpIDjOHz44YcYMGBA1GSFBw8eRH5+PiQSCZRKJRISEqDRaNC5c2eYTCbYbHVfwpDBYDD+zMTynTHFgogwfvx4ZGRk4IUXXsA999yD/fv3o0uXLti7dy+GDBmC3NxctG1bzayS53HbbbfhnXfegc1mg9/vF8XC5/Ph2LFj6N69O+bOnQuz2Vyr8CxevBgzZ86ssn/9+vXQ6XTweDxsHW4Gg8GoA7F8Z0xvunr1auzYsQN//etfMXHiRABARUUFjh8/jiFDhkCtVuOLL76IihJqomfPnsjNzQUgRCQRnE4nysrKkJOTA7vdjoyMjFrrCYfD1e73er3iIh4+ny+mPQwGg8EQiOU7Y0YWS5YsQWJiIm655RZwHAeTyYSbbroJSqUSarUaGzZsQJs2beIyRiqVIicnBwUFBSgoKEC/fv0ACIso9e7dG9988w2++eYbmEwmzJ8/HzfffHO19XTq1AmjRo1COByG3++H1WqFy+VCIBCImuOkqcyGy2AwGE2dWL4zrjSURqMRV8q79tprsXz5cmRnZ9dJKKrDarWC4zgkJCRg2bJlWLhwIbxeLw4ePIixY8di/vz5uPfee6ucd9ttt+G2226rts7PPvsMAFBWVobk5OR628ZgMBh/JhIThXUwavKdMdNQOp0OlZWVqKwUFlqfM2cOlEolJBIJ7r33XkyZMgXl5eX1Mu6RRx7BuHHjAAAZGRmYPn06XnrpJXz11VfIysrCpk2b6lxn5AtbrdZ62cRgMBh/RmL5zphiMW7cOLjdbjzwwAN46623MGzYMPj9fqSmpuK6665DMBjEjh076mXcsGHDMGfOnCr7LRYL7Ha7mEOrC5FQqqmsC85gMBjNgVi+M2YaasCAAXj77bfxzDPPoKioCA899BBuvfVW5OTk1MugSZMm4ZprrsHQoUORkpICAFizZg1mzZoFAOjatStWrlyJcDiMhx9+uM716/V6AGy1PAaDwagLsXxnXOMspk6diqlTp4KIcOTIkQua1mPy5MlV9vXq1QvDhw/HunXr8OGHHyInJwerV6+u1yp8ZrMZAOqdGmMwGIw/I7F8Z9wDEVwuF/7xj38gLy8Pubm5mDJlitiOcaEkJibiueeew/r168UxF9dee2296oo0zJw7JQmDwWAwaieW74wpFoFAAK+++ipat26NDz74AHfffTeeffZZbNiwQZxgkIgazGCFQnFB80YpFAqxUZ7BYDAY8RHLd8YUi23btuGjjz7C5MmTUVFRgY8++kicsvzpp5/G5MmTMWPGjAY3/ELQ6XSsgZvBYDDqSG2+M2abRf/+/cUJ/s5FIpHgmWeeQadOnTBu3DiMGzcOl19++YVb2wAoFAoEAoHGNoPBYDCaFbX5zguePGnMmDFISUnBBx98cKFVNRgqlYpN98FgMBh1pDbfGbdY2O12HD9+vMr+yKSAmZmZ9bewgWFiwWAwGHWnNt8Z93oWN910E3766ScMHjwYPXr0ACBM3Pfpp5+C4zjceuutDWNtA8DSUAwGg1F3avOdcYvF22+/jbVr12Ljxo3497//DSKCSqXCuHHj8Nprr4l9dJsCMpkMoVCosc1gMBiMZkVtvjNusejRowd69OiBv//97+K+U6dOITs7+8ItbGCkUmmN05gzGAwGo3pq850x2yx4nsevv/5aZT8R4frrr8f48eOb3Fu8VCoFz/ONbQaDwWA0K2rznTHFYsaMGZg0aRK8Xm/Ufo7j8PXXX2P16tVYtWpVw1jKYDAYjCZJTLH4/PPPceWVV0KtVlc5lpeXh7Fjx6K4uPiiGFdfeJ6/oPmrGAwG489Ibb4zpliMHj0aS5YsiVoGNUI4HMZPP/3U5Na6DofDFzRlCIPBYPwZqc13xvTyjz/+OJKTkzF48GDs2rVL3M/zPObOnQuLxYLRo0c3nLUNABMLBoPBqDsXJBZZWVlYv3491Go1LnWYcgAAIABJREFUrrjiCtxxxx345JNPMHLkSDz++ON4/fXXm9zypTzPN7loh8FgMJo6tfnOuDxq27ZtsXv3bkyaNAmffvop7rzzThw7dgxLlizBQw891KDGNgTBYFBcM5zBYDAY8VGb74x7nIXZbMbChQsxd+5c8DwPjUYDv9/fYEY2JEwsGAwGo+7U5jtjRhZEhMLCQvGzVquFXq+HRCJB586dMXXq1CY3piEUCjGxYDAYjDpSm++MKRZz5szBwIEDqx1nsWzZMnzwwQf48ccfG8bSBsLr9UKlUjW2GQwGg9GsqM13xhSLefPmYfDgwdWOs+jRowfGjx+PEydOXLiVDYjX663WXgaDwWDUTG2+M6ZYjBw5EosXL0ZBQUGVY0RU7VQgjU0gEIBCoWhsMxgMBqNZUZvvjGuchUKhwMCBA6sIxueff47jx49jxIgRDWJoQ0BEcLvd0Ol0jW0Kg8FgNBti+c6YYtGuXTusXbsWTqcTHTt2xBNPPIG1a9di0qRJuP322/Hss882qZlnvV4vwuEw9Hp9Y5vCYDAYzYZYvjOucRbdu3fHb7/9hkGDBuHNN9/E0KFDsXr1asybNw/Tp09vUIMvFIfDAQAwGAyNbAmDwWA0H2L5zrjHWeTm5mLVqlU4ceIEwuEwMjIyoNVqG8bKBsRmswEAEhISGtkSBoPBaD7E8p1xi4Xf78eGDRuwYMEChEIhEBG2b9+O0tJSPPjgg5g7d27DWHyB2O12AIDRaGxkSxgMBqP5EMt3xpWGKikpQffu3TF69GjodDp07NgRxcXFSE1NxYYNG/D44483nMUXSCSUYmLBYDAY8RPLd8YVWdx+++2QyWQ4cuQIsrKyAADLly/HRx99hKuvvrqBTG0Y3G43ADTJFBmDwWA0VWL5zrgii3bt2uH06dNYvXq1OB8Ux3EIBoMNZGbDUVFRAQAwmUyNbAmDwWA0H2L5zrgii3nz5iEvLw8zZszAM888g3vvvReFhYVNciyDxWIBAKSmpjayJQwGg9F8iOU74xILuVyOJ554Ao8++ig2bdqEd999F5s3b0ZpaSkUCgWSk5Nx//33o2PHjg1neT2x2WxQKpVsug8Gg8GoA7F8Z51WCFIqlRgyZAi++uorFBUVYf369Wjfvj327t2L5cuXN4jBF4rD4WBjLBgMBqOOxPKdcXedDYfDcDqdYh9cmUyGgQMHYuDAgRdsZENSXl4Os9nc2GYwGAxGsyKW74w7sqioqEDfvn2xZs2aBjHsYlFZWYnExMTGNoPBYDCaFbF8Z9xikZKSgq1bt+Ltt9/G9OnTxW5WTQ232826zTIYDEYdieU769RmYTQa8fXXXyMxMRG9evWqc5QRCAQwZswYPP744wiHw9WW4Xke33zzDT7++ON6LdvqcrmaZC8tBoPBaMrE8p1xt1lEUKlUmDZtGgYNGoQHHngAmzdvxvPPPw+ZLHZVfr8fa9asgc/nQ05ODqZNmxZ1vLS0FDfeeCO2b98OAFiwYAEWLVokDgSMh4qKCtZmwWAwGHUklu+sU2RxLj169MCmTZsgl8vx/vvvx3WOXq/HpEmTAABvvPFGleNPP/00rFYrhg0bhvXr10On0+HWW2+tkvI6ePAgli1bhuXLl2PNmjXYunUrjh49Cvw/e+cdHkW5/fHvzGzfzW56o4QgSgdBf8IFAQE7AiqCiFKUcsGCV7ErICJ68XoVFEEUC3gVUK8KCOpVqSoSAekplBBqerK7ybYp5/fHZsbd7CbZQCgJ83meeXZ33ndmzrw7c85bz4F/+pdqLFRUVFTqR126s9bmgM/ng8fjqXE6lSRJmDFjRr0EGj58ON599114PJ6g/YWFhVi5ciXWrVunzLD629/+hrZt22LFihUYP368kvfzzz/Hiy++GHT89ddfj3Xr1sHj8aixLFRUVFTqAc/zderOWo3Fjh078Omnn4Z4lM3JycGUKVPw+++/49VXX8XUqVMjFqpPnz6Ij49HcXEx9u7di86dOwMA1qxZg8suuwz9+vVT8ur1ethsthCXueHGO2w22znxOEtEkCQJoihCFEVIkqT8Dvwe+JuIQETKPvm3vFWHYZiwG8uy4DgOHMcp31mWVTaNRqN8D8zXWJDLiOd5iKIIQRAgCIJSjoGb/D/In9XLNRxyGTIMA4PBAIvFgujoaDAMc57vVEXl4iYS3VmrsUhNTcWSJUswatQo9OrVCwCwd+9e9OvXD2PHjsWNN96IF154ASNGjEBycnJEQmm1WphMJgCA0+lU9h85cgRt2rQJepFXr16NU6dO4cYbbww6R/v27TF06FCIogiv14uysjK0adOmXk4EfT4fvF5viDEIVFqCIEAURTAME6SMqytujUYDvV6v/JaVvZxHVloAlPtjGEZRctWNibxVN0Typ6xQAxWr/D3wujUZlXDfq8seTqEGKubqyrq6chdFMex+uUzljeM4aDQaZQv8HShTTd9rkjVQXlEU4fF4UFJSAp7nkZiYWOfzoaJyKRGJ7qzVWKSlpeGee+7B2LFjsW/fPmi1WkyYMAHDhg3Dm2++CQA4efIkVq9ejUmTJp2RkJs2bVIMTUVFhbJ/z549GD9+PBYuXBjSNLrnnntwzz33hJwrOzsbgH8QvjbsdjsKCgpgNBoVpSMbA41GA7PZDK1Wqyivc1VbDzQcDUGggamp1RNoYAL3R1JbD1TM1ZV1oOGRf8uKvbqxkstVznOukM/NcRx0Oh3MZjNyc3NhMBjUVf4qKgHIwwK16c46pzC988476NGjBzp16oThw4ejsLAQc+fOVdITEhKwe/fuMxZy8uTJuPPOOzF06FD885//xOjRo2EwGPDjjz/i3XffxbBhwyI+VyQ37PP5UFBQgJYtW9ZpVBobgUZPJRSO45CYmAi73a4aCxWVACLRnXVWmU0mE7Zu3YoWLVrg1VdfxYQJE4JGzNu1a4e8vLwzElCr1eLjjz/GjBkzcM011+B///sfDh48CLvdjh9++KFehgKIrN9NFEVotdomZyhUIkOj0dS4xkdF5VLlrMcsZCwWC9auXYvvv/8+ZPzgwIED9a6lrVy5Ej6fD1dffXVQN8TAgQMxcODAep0rkEjib7MsC0mSzvgaKo0bSZIa1SQAFZXzQSS6s05jQURgGAZGoxF33HFHSPoTTzxR75evZ8+e9cofKZEM0qjG4tJGNRYqKqFEpDtrO8H27dsxZ86csCd+4okncPXVV2PLli0XTewIuSlVm3UMnIWkcukhz8BSUVH5i0h0Z63GwmQy4fXXX8fBgweVfadPn0a3bt2wfft2tGnTBmPGjAmaxXQhkafi1rawpKkbC7fbjczMTGRmZiI/Pz9sHiLCnDlz0LdvX2zevLne1/j666+xffv2OvOtXbsWkyZNClmAGU6e7OxsZGdn1/rfiKKIL774AgMHDsSzzz4bkr5p0ybcfvvtGD58eI2tR9VYqKiEEonuBNXBDTfcQNdddx1JkkSSJNGwYcNo4MCByu+7776bli5dWtdpzgsvvPACsSxLoijWmEcURcrMzDyPUp09BQUF9PPPP9e4HThwgIiIFixYQPHx8QSAAJBWq6Xx48dTbm5u0PleeOEF0mg0lJqaSunp6VRSUqKkLViwgK655hpyuVz0/vvv0zfffBN0LM/zZDAY6O67765T7tatW5PRaCS3211jnvLycurTp48ic5s2bej3338PyVdSUkIjRowgi8VC//73v8nn8ylpXq+Xnn/+eWJZlqZOnUoOh6PG6+Xn51NxcXGdsquoXEpEojvrNBbHjh2j1NRUGjp0KK1YsYKsVmuQ8pk5cyY9++yzDSLw2fLggw9SbGxsrXnCGQufIJIkSedStLNi3bp1pNFoCIDyGbgNGzaM1q5dSwCod+/etGzZMlq+fDlNmzaNLBYLxcXF0a5du4iIKDc3l1iWpVWrVlFBQQHFxMTQc889p1zr/vvvJwBUUFBAAwYMoJiYmKDy8nq9BIBmz55dq8yHDx8mAHTnnXfWmu/RRx8lADR37lzKzc2lu+66i6xWa5DB4Hme7rzzTuratatiGAOZOXMmxcfH08aNG+ssy5MnT1JZWVmd+VRULiUi0Z11DnC3aNEC//vf/zBkyBCsWrUKTzzxBFq1aqWkd+nSBR9++OEZNHwaHpfLpawOr4nq3VBEhLYvfAcAMGg5WA1aRJu0MOs1iDJoYNBwMOn9+61GLaL0GkSbtIg26WDScTBoWeg4DloNA6OWg1HLQa/hoNOw4FgGHMuAZQCJAFEiCJIEXvQveos26SK6r1tuuQWHDx9GRUUFvF4vunfvjjfeeAPdu3cHwzDo2bMnVqxYgeTkZGzYsAFarRYAMHLkSLzyyito3749Ro0ahX379uG3336DJEkYNGgQOI7D6NGjsXjxYrz44ovQarUhU+fKysowevRoZGRkBM1cq2sGHM/zAPyr7WsiOzsbCxYswNKlSzF69GgwDIMVK1ZgzJgxGDlyJA4ePAiNRoPp06fj66+/xh9//BFyvs8//xyzZs3Chx9+GOQqpibUAW4VlVAi0Z0RTZ3t2LEjNm3ahPfeew+TJ08OStuzZ89F0wfM87yiKCNFkKrcVxDg8olw+UTkO2rvY28ojv5zUMR5W7ZsCcA/ZgQAI0aMQLNmzZT0QYMGYeLEifjll1/QoUMHbN26FVu2bAERoaysDCkpKQCALVu2oGPHjsp/9uKLL+Ktt97Cnj17cNVVVyEzMxOAf9ZYcXExmjVrhh07duCDDz7AhAkTUFxcDAB1Kub9+/cDADp06FBjnjlz5mDMmDEYM2aMso/jOCxatAhpaWnIyMhAr169lEG3559/Hm+//TYuv/xyJb9er4fJZMJbb72F1q1b1ymXaixUVEKJRHdGHM+iefPmeOmll0L2jxkzBmPHjq2/dOeAMzEWWo7FkVcHgRcleAUJ5S4fHG4BFV4BFV4eHl5CpVeA3c3D6RHg9Agod/lgd/Nw+US4eRE+QQIvSnDzIjy8CC8vwStKECWCKP3ViuFYBhqWgY5jwbIMJInAsg3j7iIuLg4DBw7Eq6++iuzsbBw7dgwpKSno168fpk6dikcffRQMw2Dr1q0oKyvDsmXL8N1332HVqlUA/G5brrrqKgBA165dYTKZsGfPHuzcuRPr16/HtGnT0KlTJ5w4cQJA3S5KDh8+DADQ6cK3nkpLS/HVV18hJycnJM1qtSIpKQm//vorevXqhaeffhoTJ07EY489hk6dOuGjjz7CqFGjAABDhw5FcXExXnvtNdx0002YNm0aXn755RrlEwQhotgrKiqXEhHpzvr0a+3YsYOeeuopmj59+kU5SHjnnXdShw4das0jSVLYfu/GwqlTpwgAnThxIiTtww8/JADEsiwBoAULFoTk6dq1qzLW0b17d3rwwQcJAE2fPp0kSaLLL7+crrnmGqqsrCQAtH//fhIEge69914ym83Upk0bMhgMVFBQQKIo0sGDByknJydo83g8dMcddxAAKi0tDXsfixcvpnHjxoVN++GHH8hisVBeXl5I2urVq8loNFJOTk5I2s6dOykpKSlkUD6Q7Oxs4nm+xnQVlUuRSHRnRO1xl8uFQYMGoUePHti3bx82btyInj17KrXMi4UzaVk0JQYNGgS9Xo/Fixejffv2mD17Nn755ZeQfBMnTkRubi527NiBd955B3379sWmTZuQn58fNE1ahuM4LF26FO3atcOhQ4cwYMAAJCYm4vjx4/j666/xzTffBG2FhYUoKSkBAMyaNQubN28O2niex88//4yYmJig6zidTrz22msYPnw4li9frnS9BTJ48GD069cPr776akhat27d8MADD4TEOpGhKkeLF0u3qYrKxUKDdEO53W4MHToUBw4cwL59+9C2bVsQEZ566incfPPN2LVr10XTrPf5fDV2e8hQ1Yr0xkptccnj4+NhMpnQsmVLbNiwAf3798fNN9+M77//Htdeey2ICF6vF7fffnvQJIW+ffti7dq12Lp1KwC/0q0Ox3FYvXo1pk+frgS8SktLw5NPPhlWliFDhmDz5s2YP38+5s+fD8DvC0wURXz55Zf4v//7P8yZMwfx8fEwGAzYsWMHfvrpJ/Tr1w8//PCDsspfjneh0+ng8XiwYcMGbNiwAbNnzwYRwe12w2g0wu124/Dhw/joo4/Qv3//sDKJoqh4xVVRUfmLSHRnnVp+5cqV2Lp1K3bu3IkrrrgCgL+/eu7cuVizZg02btyI66+/vmEkPksi6Y9u7MZi69atiImJCbvS8tChQ+jatSsGDhwIjuOCDEZWVhZYlkVWVpYyNiFz77334tdff1V+1xTzPDU1FR988EFEck6bNg3R0dHYu3cvWrdujc6dO+Paa6+F0+lETEwMBEFAVFQU3nnnHbRt2xZ9+vTBk08+iSuvvDLoPO+//z6mTJmC1q1bw+FwID09HR9//DHuvvtuZGRkoGfPnrjssstQVFSEZs2a4ZlnnsHf//73sDKp4xUqKuGJ6N2oqy9r3LhxNfYtf/bZZ9SrV6/6do+dM6677jq69tpra83D8zxlZ2efJ4kaHlEU6fjx4xHnt9vt9Pjjj9Mff/xBoijS4sWLyePxhOTzer3066+/0g033EBHjx4lr9dLUVFRF3wBI8/ztH//ftq/f39YuXNycmj//v1UXl5e57kqKiro6NGj50JMFZVGTSS6kyGq3ffFgw8+iG+++QZLly7FDTfcEJR26tQpXH755Th16lSDhjI9U6677joQETZt2lRjHp7ncfTo0aDplyrhOXLkCNLT0xt1SywQu92OioqKoCnHKioqkenOOge4H374YTidTowZMwZZWVlBacnJyUhNTcXOnTvPXtoGoi7FRo28G+p80rp16yZVVqpfKBWVmqnrXa/TWHTo0AHZ2dno1q0bunXrhtmzZyseCp1OJ4qLi1FWVtYw0jYAdTSUVGNxCaMuyFNRqZm6dGdEb05qairWrVuHTZs24ddff0Xbtm0xePBg9OnTBwkJCRg8eHCDCNsQ1HXDkiSpxuISRW1ZqKjUTF26s15TQ6655hp8//33yM/Px7fffovCwkKMGzfuolnbwHGc4pOoJohIrV1eoqgVBRWV8ESiO89IayYnJ2P8+PE4duyY4qvoYiCS+MpqV8Sli7ogT0UlPJHozjPWmgzDYOfOnVizZs2ZnqLB0el0tS5aA9Qxi0sZtaKgohKeSHTnWb05iYmJZ3N4gyOv5K0NVWFcuqhjFioq4YlEdzYprWk2m5XA4zXRVBWGKIr4xz/+gb59+2L37t1BaYcOHcJ9992HvXv31vu83377Le6///46HyQiQmZmJg4cOFBnaNSVK1eif//+YV2FbNy4EYMHD8add95ZZ7O4vqitShWV8ESiO8/KWBiNRvz+++9nc4oGxWQyNcmWRUFBQchalpycHEyZMgWAXwlOmDABS5YsQXZ2Nu6//364XC4l75IlS/Dpp5/i0KFD9b72tGnTsHz58lqVrMPhQL9+/dChQwd07NgR6enpYR0YlpaWYtSoUZgwYQKGDBmCV155RUnz+Xx4/vnnMXDgQLRu3RrLli1rcKOuGgsVlfBEojvr1JonT57E9u3bw24tW7bE+vXrUVpa2mBCnw1arRY+n6/WPGEHOUUeqGPa2IXk66+/xtChQ4NmK+zduxdfffUVACAjIwMff/wxNm/ejK1bt2Lfvn1BPpx+/PFHMAyD2267rV7XPXbsGHJycnDTTTfBYDDUmO/FF1/Eli1bMHfuXJw4cQK9e/fGLbfcEmQwBEHApEmTkJWVhYyMDDz22GNBs+j++c9/YvHixVi/fj3mz58Pi8VSL1kjQTUWKirhiUR31jl1dvHixZg9e3aN6Var9aLp1tHpdHXesCiKwVN9iYCXq8ZeNEbAYAOMMYDeAuitgNYA6Cz+/QYboI8CjLH+PDqT/xiNDuB0gNYIaE2ARg9weoDVACwHMCxAEiCJgMRXGScJMMVGdF8WiwUnTpxAbm6u4szx559/Ru/evQEAmzdvRnJyMrp37w4AuP3227Fw4UI8/PDDYBgGPM/jiiuuqPcUZ9k41RbtLicnB2+//XZQaNRPPvkEEyZMwKhRo3Do0CHodDrMnDkT//3vf8OGRv3yyy8xc+ZMLFmyJKLQqGdKY2xVqqicDyLRnXUai5deeilshLyLEfmGa6tBhnhXlAT/J0kAX+nfnKfOg7QAXrRHlG3YsGGYNm0a3n77bbz99tsA/F1TsuHYsmULunTpouSfOXMmOnXqhNOnT8NisSAvLw8DBgyot3j79u0DUHsc7VdeeQX33nuvYigAf0jWt99+G2lpadi6dSv69euH+Ph4sCyLp59+GgsWLAg6p8lkgsViwbx585CWlnbOvBirLQsVlfBEojublL9mvV4PIoIgCDXWokMGuDktMLPMX9sXPIC7DPDYAa/Tv/FuwFfp3+91Al4H4CoFPOX+/bwbEL3+43mX/7fg9W+SAFDAIC3D+a/H6fytDUkCIqjpGo1GDBs2DIsXL8brr7+OgoICrFq1Ct9++y0A4Pfff0dcXBw+/vhjrFixAuvXrwfg70JMSEiAw+GAXq+vd3keOXJEKddwlJeX44svvkB2dnbIA2Y2m5GcnKwYi8ceewzjx4/HtGnT0KVLF7z//vsYN24cAODWW29FcXEx3njjDQwePBgPPvggXn/99QZX7GrLQkUlPJHoznqFVb3YefPNNwlArSFfDx8+TG63+zxK1TDs3buXAND8+fNpx44dZDablfCgCQkJBIA4jqM+ffrQ+PHjCQAtWrSIPv30UwJAixcvDnteURQpKyuLMjMzgza320133XUXAaDCwsKwxy5ZsoRGjx4dNk0OjZqbmxuS9v3335PRaAwb3nbPnj2UmppKn3/+eYQlEzkHDhwgSZIa/LwqKo2dSHRnk2pZxMXFAQDKysqU79VprLXLTp06YcqUKXjppZdwzz33oG/fvkHdaTNmzMDkyZORkpICIsLmzZuxadMmZVxj0aJFaNWqVdAxbdu2hSRJWLt2bcj17rrrLmXiwsyZM3HXXXcFpffu3Rvr168PKWen04mFCxfilVdewfLly4Mi8sncdNNNGDhwIF555RV88sknQWmdO3fG+PHjMWvWLAwfPrx+haSionJGRKI7m1TLYs2aNQSAtm3bVmOerKwsEgThPErVcBw/fpw0Gg0BoBdeeIGI/C2D6Oho2rVrV1De8ePH06BBg2jPnj0UHR1NAJSN4zjS6/V0zz331Hq9t956K+g4AGQwGIjjOPr8889p/vz5ZLVaadasWfSvf/2LRowYQQkJCTRixAj6/ffflfMIgkAVFRUkiiI5HA769ttvSa/X09y5c0mSJHI4HCRJEjmdTtq9ezelpKTQyJEjG7z81JaFikp4ItGdTaplIQdgkl2oV4eIGm3LAvCHO3388cfx73//WwlEtXPnTjgcDnTt2jUo79ixY7Fo0SJ07twZP//8Mz799FOYTCb0798fHTt2RHR0dJ2L3h555BHYbDbs3r0brVq1QteuXdGrVy9UVFTAZrNBFEVYLBYsWLAAbdq0Qd++ffHcc8+FyPLRRx9h4sSJSEtLg9vtRnp6OpYuXYoRI0Zg586duPrqq9GqVSuUlZWhefPmeOaZZzBp0qSGLbwq1AFuFZVQ6tKdAFBnpLzGxM6dO3HVVVfh66+/xu233x6SLkkScnJy0K5duwsgXcMgSRLy8/ORmpoKAPB6vVi2bBkmTpwYkpfn+YvCI7Aoijh8+DCICOnp6SGB4Y8cOQKe55GSkgKr1XrO5MjMzES7du1Ug6GiUo26dCfQxGZD1WUdIwpKfpHDsqxiKAD/LIZwhgLARWEoAL/7Y3mabzhat259HqVRUVGpTiQti8bZH1MD8sBMcXFx2PSm6hdKRUVF5WyoS3cCTcxY2Gw2GAyGGmNsNObxCpWzh2GYOqOBqahcitSlO4EmZiwYhkFKSgry8/PDpjeFbigVFRWVhqYu3QmcJ2NRVFSEp59+GqNHj0ZmZmad+YkIJ0+exLJly5CTk1Ova8XExKC8vDxsmhpW89JGbVmoqNRMbboTOA8D3Lt378aAAQNQWlqK+Ph4fP/99/jPf/6Dm266KSjfm2++iWXLlgEAPB4PsrKyAABRUVH45JNPMHTo0IiuZ7Vaa506q3ZDXbqoxkJFpWZq053AeTAWc+fOxdVXX40bb7wRw4YNw3fffYf77rsPGzduRMeOHZV8P//8M3bt2oX+/fsjLi4Ojz76qDIYfdVVVwWdMzMzE1lZWWBZFnq9HtHR0TCZTOjcuTOsVivy8vLCyiIIgjrAfQmjGgsVlZqpTXcC59hYnD59Gl9++SU2btyIXr16AQCmTJmCzz77DGvXrg0yFj/99BNGjBiBlStX1nnezz//HC+++GLI/tzcXMTFxeGPP/4IexwRqWMWlzCqsVBRqZnadCdwjscs1qxZg44dO+Jvf/ubsk+SJFRUVITMu09PT8fXX3+N7t274x//+EetUZtqWnl86tQpJCcno7CwMKxSEEWxyXdDVVZWYteuXdi1axeOHz8eNg8RYcaMGejRo4fiobY+rFy5MuIIiVu2bEHPnj3x9NNP16ioT5w4gUWLFiku0atTUFCADz74ABkZGfWWNRDVWKio1ExtuhPAufUN9dprr9F1110XtO+dd96htm3bktPpDNr/0UcfkdVqVXwQpaWlhfVKSkT02Wef0dChQ+m2226jG264ga6++mpq164drV69mubNm0cAqKioKOS4EydOUHl5ecPd4Hni9OnTtG7duhq33bt3ExHRG2+8QTabLcgH1H333UcHDx4MOt9TTz1Fer2eWrZsSWlpaUFl9eabb1LXrl3J5XLRwoULQ7y/8jxPOp0uIt9Nv/32G5nNZrriiisIAH399dcheV5++WXSarUEgLRabYhvmsWLF5PBYCAAxDAMrVu3LuJyq05j9TisonI+qE13EhEW681vAAAgAElEQVSdU2Px1ltvUZs2baiiooIkSaIVK1aQzWajzZs3ExHR//73P5o8ebKSX5IkkiSJcnNzKT09nW655ZZ6X3P58uUEIKyhycvLCzFSREQ+0XdRO5j74YcfSKfTEcMwpNfrQ5z7jRgxglavXk0AqH///rRy5Ur66quv6LnnnqOYmBiKjo6m7du3E5FfYbIsS2vXrqXi4mKKi4ujp556SrmW7N68oKCABgwYQDabjfbu3auke71eAkCzZ8+uU+4+ffrQPffcQ5Ik0X333UdpaWnk8/mU9FmzZhEAuu++++j06dP0008/UV5enpL+7rvvEgC67bbb6OjRo7Rt2zbat2/fGZdjbm4uVVZWnvHxKipNmdp0J9E5diQ4atQozJ07Fx06dIDJZALLssjIyFC6oJ555pkg1xXytNZWrVohMTExKOZ0pMixmysqKkLSwi3KIyJc/Z+rAQB6To8oXRRsehvMGjMsOgsMnAEmrQlRuihE6aJg0Vpg09tg09lg1Bph4AzQclpoWS2MnBEGjQE6Tgcdp4OG0YBlWLAMC4kkSCSBl3gIJICIYNPbIrqnG2+8Ebm5uXC5XHC5XOjatSsWLFiAq666CgzDoHv37li5ciWSk5Pxww8/KG4+7rjjDsyaNQvt2rXDvffei8zMTPz222+QJAk333wzWJbF6NGjsWTJEsyePRs6nQ5mszno2na7HWPGjMGOHTuCph3L7gFqwm63Y/fu3Zg0aRIYhsE//vEP/Oc//8G6deswdOhQbNiwATNnzsQDDzyA999/HyzLIjk5WTl+7969eOihhzBkyBB88cUX0Ol0SEtLi6i8akLthlJRqZnadCdwjge44+LisG/fPsybNw82mw1///vfYTKZlPTffvsNGo0G5eXl+O233wD4Q3h+//332L59Ox5//PF6X1N2ROdwOELSwhkLgfxhVSWS4BbccAtuFLoK633dM2Hv2L0R55WNqrzC8vbbb0ezZs2U9FtvvRUTJkzA5s2b0a5dO/zyyy/YsmULiAjFxcVo2bIlAP8YQufOnZVymDFjBubNm4c9e/bg6quvRnZ2NgC/D6rCwkKkpaVhz549WLx4MSZPnozCQn/Z9O3bt1Z59+zZA4fDoYR7veqqq3Drrbdiw4YNGDp0KB566CEkJCRg/vz5YceRHn30UWi1Wrz33nshjgfPFJZlIUlSg5xLRaWpUZvuBM7D1Nno6OiwM5eAv8J1njhxAmPGjEFJSYmS1q9fP8ycObPe16vthilMfFktq8XuMbvBSzx8og92rx1OnxMVfAUq+Up4BA9cggt2rx2VfCWcPifsXjscPgfcghsewQOf5AMv8vCIHrgFN3yiDz7RB5FEiAFhVTmGg4bVQMtqldYGyzTMgHtsbCyuv/56zJkzBzk5OTh58iRatGiBAQMG4KmnnsIjjzwChmGwbds2FBUVYcmSJfjuu++watUqAP7/4Oqr/S2sK6+8EiaTCfv27cOff/6JzZs348knn0SnTp2UFZ51LW7ctm0bAP/MtS+++AILFy5EaWkphg0bBgA4evQopk+frtRmqnP06FFMmjQJSUlJDVI+gN+hoWosVFTCc8GNRSTISmjHjh346aefEBcXh7Fjx8JoNNb7XHLLpbKyMiStNt9QWtbflWTWmsOmNwaGDx+OcePGKWtJnn76aTz00EMh+fLz8zFp0iT07NkTU6dOxZtvvokdO3ZgyJAhOHToUFCkLJ1Oh4cffhi7du3CjTfeiKSkJBiNRqSmpkIURWRlZYV07bRp00b5PmfOHDRv3hwjR47Ejz/+iB07dgDwN3mPHTtW473UlX4mMAyjGgsVlRqoTXcCF4mxAACNRoMePXqgR48eZ3Ue2To6nc6QtKbuSHDQoEEwGAxYuHAh3nzzTcycORMdOnRA//79g/JNmTIFL7zwgtK1tXPnTmzatAmnT5/G4cOHQ8IqsiyLJUuWYP/+/cjIyMDgwYMRHx+PvLw8/PzzzyFyxMTEAPCPPX333Xe47LLLoNVqsWTJEkycOBFHjx7FI488gpkzZ2LixIno3r27cux//vMfXHvttXjkkUcwadIkfP/997j55puV9FWrViE9PV3p3qoP6piFikrN1KY7gYvIWDQUUVFRAGq+4cbuG8rj8dSYFhsbC6PRiObNm+Pnn3/GwIEDMWjQIKxbtw7XXXcdiAgejwdDhgwJmljQr18/rFmzRuk6qr5iHvAbjNWrV2PWrFl47rnnAABpaWmYOnVqjXL27t07KNBUv379AAAulwtPPPEEtmzZgptvvhmPP/44brnlFrzzzjtYvnw5cnNzcf/992P9+vW4++678dhjj2Ho0KH47LPPMH/+/Ij8i4VDHbNQUamZunRnkzMWcteVy+UKSWsKtcpt27YhLi4O0dHRIWmHDh3C//3f/2HgwIFgWVYxGLfeeiuysrKg0WiQnZ2Nbt26BR133333KRMMAAQNnAeSlJSEhQsXRiTn+vXrMWjQoKB9cuhVs9kMo9GI1atX44MPPsB7772HZcuWITY2FmvWrEF8fDwA4JNPPsEnn3yCd955B59//jksFgu+/PJLXHbZZRHJUB21ZdF0kXw+iCUlIFECo9OC1etBPA/+5En4jp+A71gexJLSvw5gGIBlqiqPDEiSAAYgrw/kcYN4HsQLYG1WaBOToElJhjY5GUJhEbyHD8N3+DC8R3PBMCw08fHQpKZAm5QELiYWrNEARqcDo9ODs1mhTUmBJiUVnOXi7uKuTXcCTSysqozRaMTDDz+Mf/3rX0H7m0JYTUmSUFRUFPHAb2VlJV5++WUMHz4cV155JZYtW4Z77rlHmVwgIwgCtm/fjpdffhmLFi1CUlISEhMTsW3bNrRt27becv7666+wWq3o3LlzyHUulMuV0tJS8DzfoIPmKucXoawMvtxc+I4cgffwEXhzcuA7dgz8qVNAHTHlLzSs2QxNUhIYgx4MwwIaDhAlcHGx0DVrDi4mBlx0NFiTEVxMDFizBVx0NLhoGzirFYzBEKS7iAgQBIAIBACiCBJFQBDAhalMRkJNuhNogi0LwD9QU5u7kMYMy7L1UnZmsxmvvvqq8nvcuHFh82k0GvTs2RPffvutsm/Pnj1o0aLFGcnZu3fvGq9zoWgMA9zE8yCfD+A4MBqN/7Na5UYsL0dlRgZcv2+D648/IBQX+xVRfDw0ycnQJCRAqqiAVOGEaHdAtNtBPA+GYwGWA1M1AYLR6fzn12rBRVnAmkxgo6xgDXpoUlJg6NAB+suvCKoRExHI5wP5fH/Jx7IAy/o/w92TJMF39ChYswWamGj/davuw5OVDU9mJjyZB8CaTNCmpEKb4r8H0eGE71ge+GPH/bX5o0chlpaGvQZYFpqEBECj8cvn9YLhOGiapULXrDl0rdL86QwLkH89K0lS1bJWAsOx/tmSOh1Yg9HfMuBYiHY7+IICCKfzwZ86BS46Gob27aBLbw39Za0BloVQUAD+dD6EwgKI5eWQ3J6qMvJCLC8Hf/IU+Px8SJWV8B05Elb88EPKoTBard8wSFKNxpGNikLbP87MNU5turNJGguLxVLjwhKVyJHXZjQVLsSYBRH5DYDHA8njAXk8foVk8StnMAx8hw6hYvNmVP76K1zbd/iNhQzDKEoZLAuG4yBVVFQpvL8Qy8rAnzjR8DfAMNA2bw5Go4HodEJyOkFeb2g2vR7Gzp1h6NoFxs5dwMXGwJuVDdf27XBlZEAMiJPAGP3KWKrFHXaN4phM0KenQ5eeDv1lraFr0wb6yy6DNjUVLlaASKIyRV0iCWXeMhS7inGy4iROV55GubccPtEHhmHAMiwY+A2xRP5YN17RC4/gAS/xkCQJthgbkjokIcXcC8nmZDh9TmSWZiK3/EfkZuZCkAQkmZOQlJyEpNZJsOmbw8AZoNfoYdVZEW+MR4IxATH6GLDOSojFxZC8PkASQYIIhmMhFBWBP3kSot3uNzYuN8SyMoiVFZDsdgjl5ZDsDr8Bqr5QmeP8XWoMA6bq+WDNZ97dVZvubJLGwmw213jDjbkLSuXsYFm2RieU1SFRBH/6NMSSEkCjAavXg9FoILlcEIqLIRQV+z9Liv0vMBEg+g2R6HBALCuDUFgI/tSpYOUfCMOA0etB1SYtMCYTwPMguYuB54EqJUEAoNHA1K0bTD17wNyjB3StWkGqrPQrndOnIRQVg42ygIuygrNGgYv21+ZJlBQlBQDE+/xdFzwP0eGE5HJBdNhBHi98uUfgyTkI78GD4Ks7pNRqwWq1IFH0185FEeT1+g3D9u1hb5VLiAdEyd/KcbtBbjcYoxH6yy+HoX17GNq3A/l84PMLIORX3YPFAm2L5tC1TIO+dTp0rVtDiotGgbsQRxx52F+yHwfLvsehPYdw6rdT8Ig1T/4IxKQxIcWcgjhjHHiJV9ZGeQQPdJwORo0ROk4HPacHGP+0egYM3IIbXtELN++GBAlaVus3SJ4yOLwO5Dny4OJdyHflw+4NNYTR+mjEGeOgZ/VgWRYcw4GIkGBKQEr7FNj0NkTrr4BRY0SMIQZmrRnR+mjE6Kyw6q3QMVr/s8BUjbVoNCCQv0ICgkQSBEkAL/Fh15RFQm26s0kaC61WW6OrkDMtRJXIISK/8qwq55B+VnkLggHC/S3yi1Hbdfw/wmVQZADLguM48F4vfHl5KF26DL5jxyBVVPi7HDQcwGlAPA+hoAC+EycUBX3WaLVgdTowRqN/4NXng1RZCcnlAnk84KKjYenfH+bevWHu9TdwVVOPAQCS5DcaogiSCBAFQKfDSb4IGcV7kXF6DQ5sPQAAsOlsSIhOQFxKHJy+o3DxLrjcLpSXlaOSrwTHcmDAgGM5aBgNYg2xsOgsiNZHIyo5ClpWC5PWBA1jRNqQO5BmTUNLbQzEk/7xANZqAxdlgaTXQYKkuLJhwEAsL4d71y549u6Fe+8+iA479K0vg7HblTD36AE0T4FIInSsDnB5QF4PRKsZp1ynke3IQ54jD7zEw6RphjhjF8QZ4uDknThVcQrHHMeQ59iCI78cQYGrABL91TpMNiejlbUVuiV2Q4uoFjBpTWDBgmEYaFgNjBojTBoTLDoLzFozUswpsOqs51wHeAQPClwFKPOUKQt0ZS8R8gLfSr4SXtGLQlch9pfs9xsdnwMGzgANq4Feo4eG0cDhc6DUUwqP4IGW1SoGQvY+EY4NIzYg3hhfb7lr051N0ljodDr4aqrNqZwVFKicqzaSCCCpBiNQVRuWX84zmE8RdPwZnoPR68ExDES3G778fJR99lmdx2gSEqBJTPTXoH0+kCiA1Rv8YwMJ8eDi46GJTwCj01Z1A3AACKwlCprYGHDx8dA1bw4YDYqCY8CAQGDAgCVAqqwEr+ewp3Q/tp3ehk2/fIScshxUDVmCZVhoGI2i6DWsBl7RC68Y2hUUUTnAb3wDFW5t6Dk9Ui2p4BgOdq8dFXwF3EJon3aCMQFdErqgc//O6HDX/TBpTTjmOIa9xXuxfdcKHNx4UMlr1pph4Awo85bVKEeU1u+jLcYQA5vehgRTAjrEdUCyORnJ5mQkmZLQPKr5RbuI1qAxIM2ahjTr2fkzC4SIFHdEpytPo8hdhEJXIXSsDrHGWCSZkpSy0XFn5iKnNt3ZJI1FbdbxUiSoNi8r+NBMwcpe/qxeAzvTyXNBx/mnLdZ63rp+ByLLWEdtkeU4EMtC26IFEp9+Grq0luCio/2GQBD9tXaNBpr4BOhaNIdo0MIjeCBIguIIUq4Zyu5g7F47nLwTbt6NCr7C7zLGZ0d5RTmKi4pxavspVPLhhy9ZhoVZY0alUBmkNBkwiNHHIN7k7++26WwwaU3+WrLWBIvWgnhjPOKN8Yg1xCLGEAOjxu/UkmM5sIx/sJaXeAiSv/ap5/T+VkBVGflEH8o8ZX73NT47eIkHx/iP1XN6uHgX7D47yjxlivyBLmpcvAtl3jKUecpQ5i2DW3AjvzIfpypOYV3uOgCAgTPAorPgsujL0C2xG6IN0Yg1xCLOEIc4Yxy0rFa5Zw2rgUljgk1vg1VvDUpT8cMwDExaE1rZWqGVrdU5ucYl17LgOC5s37Q8z74pd0MpNX+hqj+ZpL9mrMj93/U7IRitFpCPFwT/TIyqQbXAwbVApR20piFQ0VflIZC/ySAfErYPKliO6ucISg41f9VgwHIcJA0HPjUev/SJwVHHXpyqOAWP4IFH9IBjOAg+ASXZJTix/QRcQvj55nURpY1CnDEOSeYkdEnoAq/oRZGrCEXuIhS7ixGli0KaNQ3NLc3RPKo5ks3JiDPEwaa3IdYQi2hD9FkrS4ZhFO/H4dBxOv/ArFmdRqzyFzXpTqCJGguWZcMuvmpqRiKwz16etijfI3EsIPBg9PrgKY0cpwySBcL4D/5rUxKCxwwYjlMG1GQZAmus8j6P4FEMs47TBTlMJCLwIh9xV4h/OCPg/LJhqEcjR1aavMjjqP0oXtz6olLrlrHqrDBqjOAYDknmJJg1Ztj0NqRYUtDM0gyp5lQ0j2oOiSR4RA+8ghcSSX6X9Xqbv+9fF3XGXQAqKheamnQn0ESNhSRJNc7nb8xrEIkIJIpgqubeMwGKXRAEPPLII/jzzz/xzjvv+GNdVK3IzM7OxvTp0/Hcc8/hyiuvhKThwipqguS/RlUZ6Tk9GIbBL7/8gscffxy9e/fGG2+84Z9iyHsVQ8AwDI4fP47Vq1ejd+/euPLKK6Hn9PBJPuhYf3p+fj6+/fZbtG/fHr1794aO00EiKWg2h0y4Vobczy9/r8oY/piA1orcgvGXlb+7I8GUgH/3+zesOitiDbGKstewTfJ1UFGJmNp05zmNlHeh+Nvf/kYDBw4M2Z+dnU08z18AieqPJEkkCQIREZ08eZJ+//33oPTMzEx64IEHlLyjRo0iq9VKKSkp1KVLF6qoqFDyPvXUUwSAvvnmGyIi8gpecvGusJubd5ObdxMv+stp69atZDabqX379gQgKMyqHF1wzpw5pNFolFCuv/76a5CsgaFRAdCqVasauLQiJycnJyhan4qKyl/UpDuJiJqkC9aaXErUtCiLeP6iaHFQ1ZiA5PWCPB7/0n0Aq1evxrBhw4JmKezdu1dZbf3777/js88+w+bNm7F161ZkZWXh/fffV/L++OOPYBhG8dWkZbUwaoxhN4PGAIPGoNSyn332Wdx22204cOAAHnjgATz22GPwVi3KYhgGs2fPxvPPP4+RI0eioKAA69evD/It9d577+Hvf/87rr/+euTl5WHnzp1o3br1uS3IWqitT1ZF5VKnNnc8TbLd7fV6Q3wfAeEdyRERsrpe6U83GMBFRYGz2cCazWCjLGD1Br8bBKsVXFQUWMtf/lpYoxGMvsppmFbrdyBmMPgXcOl0frcKVStv5eX5JAjKIDFrs/01O0kUQ5fvV3UxWa1WnDx5EkeOHFG8uK5fvx59+vQBAGzevBkpKSno2rUrAODOO+/Eu+++i6lTp4JlWQiCgHbt2ikPQaRjNw6HA3/++SfmzZsHAJg6dSo+/PBDrFmzBnfddRc2bdqEGTNm4P7778eSJUvAsiwSExOV4/ft24eHHnoIgwcPxpdffgmdTnfBV4WrnmdVVGqmJt0JNFFj4fF4YDAYQvaH9ToqVA1yShLI5YLgckEoKDgPUgLtszIhVXc5XrVkHxyn9M3feeedSEpKwltvvaV4fc3Pz0f79u0B+EOlyoYCAKZPn46OHTsiPz8fZrMZR48excCBA+st3759+2C325Vzd+3aFUOHDsXGjRtx11134eGHH0Z8fHyNoVEfe+wxcByHxYsXN1ho1LNFNRYqKjVTk+4EmqixcLlcQbG+ZcJ1QTBaLdof2A/ieUheHyR7ud8HTkUFxIoKkMfrd4Ngt//lnK28HKLdAcnthuRxg3w8iPeB3FX+f7zeqkVc1VoLVc7hGK3WbwwCp6BWGQliAEESIYpeEBH0nB4GgwHDhw/Hu+++izfeeAMFBQVYs2YNJk+eDADIyMhAdHQ0Fi9ejJUrV2LDhg0AgOPHjyMpKQlOp7PGB6A2MjL8zshWrFiBL7/8EgsXLkR5eTmGDBkCAMjLy8OTTz6p+MGvTl5eHsaPH4+UlJR6X/tcobopV1GpmZp0J3CJGYvaapWMVgtOqwVnMaO2Ge5UffFatWmmIeet7uqiOnodiAgSJIiST5luyjB+twzyzJ4pU6ZgwYIFWLRoEfr16wej0Yjrr79eOc3Bgwfx8MMP47rrrsOUKVOwaNEi7Ny5U1HkAwYMCHs/oihi//79IbJdfvnlyvfXXnsNLVq0wH333YeffvoJO3fuBOAPlpKXl1djWdWVfiFQWxYqKjVzyRkLn88XttvjTGuVRPSXr/hARcOyfo+gLFs1BVSCJE8/DZwSGuaSskGQ/diw8Psukt0xVKdDhw545JFHMHv2bGRnZ6NPnz5KrG0AeOmllzBlyhTEx8eDiLB+/Xps3LhRGdd4++23kZKSEjR41aFDBzAMgy1btoRcTw6t2rJlS/z4449IT0+HVqvF0qVLMW7cOBw6dAhTp07FM888gwkTJqBnz57KsZ988gl69eqFqVOnYty4cVi9erXSGgH8oVFbtmwZEoTpfKDRaCAINfvUUVG5lKlJdwJomlNntVotPf300yH7T506RaWlpRGfRxJFEj0eEl2u4M3rJUkQSJTEv6ah+vybh/eQT/D5N9FHvMgTL/IkiAKJokiiJCpTTuvLyZMnSafTEQCaMWMGEREJgkA2m4327NkTlHfixIk0aNAg2r9/P8XGxirTVgGQXq8ni8VCo0aNqvV6r7zyCo0cOTJoX25uLgGg3bt3k8fjoVtvvZXi4uJo9uzZtH37dpowYQKZTCYqLCwkQRBozJgxZLFY6LnnnqOMjAx64oknSKPRUE5OzhmVwdlSWFhIhYWFF+TaKioXOzXpTiKiJteyEAQBPM9HPGYRDpLdYgTmDRh4lkgCTwIkwR+KkWM4xSfPuVwlnpqaiieffBKvvfYabrzxRgDAzp074XQ6QyLSjRs3DgsXLkSHDh2wYcMGrFixAkajEQMGDEC7du1gtVrrLIv169cHdXUBQKtWrXD99dfDYrFAr9fjq6++wtKlS5XxkpiYGKxduxYJCQkAgA8//BA33HADFixYgNWrV8NsNuO///1vUDfX+USeHaaiohJMbboTaIJhVe12O6Kjo/H6669j2rRpQWklJSUQBKHGSHMkdzcF+k+qiiQmkaS4GSYisKzfG+i5NhDhZCwuLlaUsc/nw/LlyzF27NiQvGcbwnTbtm0wmUwXVWjUs6WsrAwej+eiGnRXUbkYqE13Ak1wzKK0KuxiTGBMgCrqGrMIak2wrN9IgOCrcgfNMiw0rAYcExrq8nzBMIxiKAC/S+FwhgI4+xCmPXr0OCfnvZCoYxYqKuGpTXcCTdhYxMeHBv6oayYMo9GAJMn/ybHgqxzN6ThdjQPPKo0LdQW3ikp4atOdQBM0Fg6HA4B/1XN16qpVEuMPkiNIAkgSoGE1Qd5SVRo/HMepU2dVVMJQm+4EmqCxsFcFgbfZbCFpdcVgZuCPIKZhNWoroonSxIboVFQajNp0J9AEjUVZWRmA8P1uBoMBgiCgqKgI8fHxIQaBYRhwDBdy3MWEJEmQJAmiKEIQBAiCAFEUQUQQRVFJk/dV3yRJCvqsDXktSOAG+GvnXJWbdI7jwLKsPwB91T6WZZVPOT3wmAtpiB0OR42zPVRULmVq051AEzQWFRUVAACLxRKSxrIsWrVqhfz8fGRnZyurpasrNXlfOGUn7w9UoIHKEQh11CcrauAvZS8rbPm7rOgDv0uSpBgEQRCUdFkWjUYDjUYTJLNOpwtRzNVlDZRfRgmaRBTyGbgBUIyRLGOgrLKM8v0FGjA5H8Mw0Gg0QeVa3egEfgaWbzjjFa7Mq98Dz/MoLi6Gx+O54M4MVVQuRmrTnUATNBZutz+YvLEq8E91tFotWrRoEdRvXV2ZBW7yfp7ng/KEq61XV6oygYqtuvKTv8uKUf4uK1PZIMi/z3XNXD73ubqGXD6yYZENT2DZ+ny+kP+kprIOPG9t96LRaGC1WpGamhrW6aGKyqVOXbqzyRkLu90OjuPq7GoIVBiyUlY598jG7mLxQquiouKnLt3Z5KpYTqcTUVFR6gC1ioqKSj2oS3c2OWMhr0JUUVFRUYmcf//73ygsLKwxvcm5+wD8A7CBHlkbE0QEu92OkpIS2O12VFZWwm63o6ysDCUlJXA6nfB6vfD5fPD5fOB5Hi6XC5WVlXC73fD5fMoMqUACx0J0Oh20Wi00Gg20Wi20Wi1MJhNiY2NhtVoRFRUFm80Gs9mM6Oho2Gw2GAwGGAwGmM1m2Gw2aLW1OXJvvAiCgPLyclRUVKCyshIOh0MpW7fbDY/Hg4qKCjidTrhcLmXz+Xzwer3weDzgeT5kYkLg7DO55iaXe2DZ6vV6aLVaWCwW2Gw22Gw2WK1WWK1W5XtiYiJsNlujbT07nU6UlpaisrJS2VwuF5xOJ5xOp1K+8ne5TD0eD7xeL3ieh8/nC3rG5XEpnU4HnU4Ho9GIqKgoZQssv+joaERHRyvfY2JimsTz7PV6cerUKZSVlaG0tBQFBQXK8+vxeJRn1ev1Ks+0/KzK44NdunTBv/71r7Dnb3Id9Y8++ij27dsHo9GI6OhoxMbGKsrPaDTCYrEgJiZGeXBiY2MRGxsLs9ncYOMWkiTB7XbD6XTC4XDA5XLB4XDA4XCgoqICBQUFKCgoQH5+PkpKSpS0srIynD59Gp7q0fOqIff5B74YZrMZRqMRer0+ZDaUPMPK6/VCEATFyMiOw2SDU15eHvGCNYPBgOjoaMTFxcFiscBsNiM2Nhbx8fHKS5iYmIi4uDiYzWblZZVfUqPR2ODKzufzoaioCKWlpYqiKSkpQUlJiaJ0KioqUFZWBofDAbvdDqfTqSisiooKFBcX12vRntFohNFohE6ng4hg5W8AABAlSURBVF7vD1QlG+LAiQnyBkAZqHe73SgoKFCMkMvlUhRjYLz1cOh0OiQmJiIhIQGJiYlISUlBUlISkpKSYDKZEB0djfj4eMTExCA+Ph7R0dGwWCwNNrhPRPB6vUpFRVb4ckXn9OnTyM/PVz7z8/NRWlqq/BeRoNfrYbFYYDQaodFoYDAYFGMqz/qTnyFRFOHxeJRKlMfjUd4/eeC2NkwmEywWC6KiopQyjYuLQ2xsLEwmExISEhAfH6886zabDTExMYrhaYhyJSL4fD64XC5UVFTA4XCgqKgIZWVlym/5nuQK5OnTp1FUVITCwkIUFRXVen55PEKv1yv6IvBZ5TgOLperxuObXMvi0Ucfxfbt2+HxeFBaWory8nI4nc6IXDxotVro9XrodDqYTCal1qfX65XClF2GiKKovNQ8zyvKRn7h64LjOCQmJiIxMVExZtHR0UhOTkZKSgri4+OV2r3NZkNsbCxiYmJgtVqh0ZybRYOSJCk1vPLyclRWVqK8vBx2ux0ejwcej0dp6ci1w9LSUqUWXlJSgtLSUjgcDni93jrv32w2K8ZOVghyS6f6dGXgrym7ssGTZfL5fKioqIhICcmKVK61R0VFwWQywWw2IyoqSvlPzGazsk9+seRNVioGg+GczazieR4OhwPl5eWKkrDb7bDb7SgoKEBhYSEKCwtRXFysKOTCwkLwgU4wq8EwjGKoZYWr1WqVZ7z6OpnA2Wlut1tRYnKttC7VIcdkT01NRXJyMuLj4xEbG4vU1FTExcUp5W42m2EymZRWrcVigcViabDaviiKQZWD8vJypVzLy8tRVlam6Amn06mUa1FREcrLy2tVoIHlajablXKV9YisjOWejsBn2Ov1wuv1wu12K63ZSNSxRqNR9EVSUpJSts2aNUOzZs2USkJSUhJsNpuix7Ra7VnpjSZnLMJBRHC5XHC73UrN0m63w+FwoLi4GGVlZUrNSO7ikZtsctM3cPGb3KUT+MLJD7hcyzeZTEoTWK5ZW61WWCwWJCQkIC4urtF2I0SCy+VCYWGhUrayogtUfhUVFYoikmvU8lZ9cSEAxYDI3Q1y941Op4PFYkFsbKxSA5SVTkxMDBISEmA2m8+pcj8XCIKAyZMnK/c2Y8aMWvNLkqR0O8hdEXLLKrD85e4HuaIjP+PVF3LKhkOv1wcZSvn5lp91+bf8nMfFxSlG92Iob0EQMGLECKXFO2/evHodL0kSiouLlVZRYNdweXm5UimtrKxUnl+5EiO34OXWauAzrNfrodfrlQqMxWKBwWBQdIdclrGxsbBYLIoxPRet8ki4JIxFpAiCgL179yrKJy0t7UKL1ChxOBwYOXIkkpKSkJiYiLlz515okRolZWVliI2NBQCYzeaIu29UglHLsWFocmMWZ8OBAwfQvXt3AEC7du2QmZl5gSVqnBw9ehTfffcdAH85qsbizDh+/LjyvUWLFhdQksaNWo4Nw4VvI15EBPZN1rTkXaVu1HJsGNRybBjUcmwYVGMRQHl5ufK9JmdaKnWjlmPDoJZjw6CWY8OgGosAAmfw6PX6CyhJ40Ytx4ZBLceGQS3HhkE1FgEEzq9vrIv6LgbUcmwY1HJsGNRybBjUAe4A2rVrh1mzZkEURbRr1+5Ci9NoUcuxYVDLsWFQy7FhUKfOqqioqKjUidoNpaKioqJSJ6qxUFFRUVGpE9VYVOPAgQNwOBwXWoxGh9PpxB9//BHWAV5JSQkOHjx4AaRqepSWliInJ+dCi9EoOHLkCF566SUUFBSEpPE8j9LS0gsgVeNFNRZVHD58GAMHDkTHjh2Rnp6OTZs2XWiRGg0HDhxA3759cc0112D69OnKfkmS8NJLL6FVq1a44oorMHnyZAiCcAElvXiZPn06iouLAfh9mc2aNQunT59W0okIc+bMQatWrdC2bVtMnDhRLcta+Pbbb9GuXTvMnz8/JEzoV199hY4dOyIxMRGPPfZYvbwMX9KQChERXXbZZWS1Wqlly5b0z3/+kzQaDa1bt+5Ci3XRs2XLFrJYLHTllVdSt27d6JprrlHS5s2bRxzHUUxMDD355JPUtWtXuu2220gUxQso8cWH0+kknU5Ha9euJSKirVu3EgDasWOHkmfBggVKWU6bNo26detGN998MwmCcKHEvmgRBIFSUlIoPT2d8vPzg9LWrVtHAAgA3X777dSpUycaPHiw+kxGgGosiOjHH38ko9FIFRUV5Ha7iYjoo48+IrPZTC6X6wJLd3EzYsQIuuOOO0gURZo1axb16NGDiIh4nqeOHTvSW2+9RT6fj3iep6KiIkpLS6OFCxdeYKkvPhISEhRjkZWVRWazmZYvX05EfuXXpUsXeuONN4jnefL5fFRSUkLp6en01ltvXUixL0p++OEHAkBz584NSbv99tvpwQcfJJfLRXa7nQoLC+nyyy8Pm1clGHWdBYAPPvgAY8eOhdlsVvb16tULlZWVahO1DpYuXQoiQllZGb744gtMmTIFAJCRkYFjx47h3nvvVeISxMfHo23btuqYUDXkAEwybdu2Rb9+/ZCVlQUA2L59O44cOYLRo0crAbpiY2PRvn17tSzD8OCDD8JiseD+++8P2p+fn4/Vq1fjzz//VFyuA/53PdDZoEp41DEL+BXbddddF7Tviy++wMCBA4MMiEooBoMBRqMRr732Gsxmc5Cx6NKli+IaGvAPOG7fvh2DBw++UOJelOzfvx8ulwstW7ZU9jEMo0R4y8jIQKdOnRAfH6+k5+XlYdu2bRgyZMh5l/dihohQWFiIO+64AwkJCUFpmzZtQlpaGrp06aLsczgc2LZtG2688cbzLWqjQzUWVQS2IDZv3ozXXnsNb7755gWUqPGQmZmJjz76CFOmTAkKyhJYpi6XCxMmTMDIkSPRoUOHCyHmRYsc/rZly5bwer3IyMhAVlYW5s2bpzyDgWXpdrsxYcIEDBs2DJ07d75QYl+UbN68GU6nE5988gk0Gg2MRiMeffRRZfJAIESEZ599FsnJybj11lsvgLSNjAvcDXZRcMcdd1D37t1p9erV9Oqrr1Lr1q3pv//974UWq9EwZswYuummm4L2rV+/njQaDb399tu0cuVKuuaaa2js2LHKmJDKX/h8PoqJiSGbzUYxMTHKAKzJZKJVq1bR5s2bieP+v717DWnq/+MA/m4T2VwLNVTShClSGhYF0m2QqQ/ymoSoK7PoghMybGX1pCAzdCnVrCfL7EFgoDYWSiWJUTPt4mUSQaPAsnBmeSsSL1vu838gjf+YNv39DPPn5wWCnn3P2eecB753znff71dIGo2GqquradOmTZSZmcnXcgopKSkEgIqLi+nmzZsUExNDACg+Pp7a29tJKBTSrVu3qLW1lTIyMigiIoJ6enrmu+wFgcOCiHp6eig+Pp4AUFRUFHV2ds53SQuGXq8nALR8+XJKTk6m5ORkqqioIJvNRleuXCFPT0/y9fWl8vLy+S71r6bVaikpKYmOHz9Oer2ewsPDSalU2l8vLS0lLy8v8vHxobKysnms9O+mUCgIAD18+JCIiMbHx8nLy4sSEhKIiKiwsJDEYjEJBALKzc3lb5PNAs8N9X/MZjMCAgLmu4wFxWAwIDc3F+vWrUNgYCAMBgN27dqFEydOAJhcS8DNzY0XnZmlxMREWCwW1NfX27d9//4dQqGQr+VvPHv2DHK5HOnp6di4cSOqq6thMpnw+PFj+yqYX758wcjICIKCgua52oWFw4Kxv1BZWRlWrlzJz9JniYhw/vx5FBYWwmazIS0tDSqVChEREfNd2oLHYcEY+8/p6+sDEcHX13e+S/nP4LBgjDHmEn91ljHGmEscFowxxlzisGBsFn7+/Amr1Trt6zabDUajEe3t7b+dKsZqtaK+vh5arRYjIyN/olTG5hT3WbBFrbu7G0ajER0dHeju7nZ4zWazITg4GCkpKbh8+TKMRiM6OztBRJBKpVCr1dizZ4991Pq3b9+wY8cOtLS0AAD8/Pxw+/ZtxMTEOBy3q6sLsbGxePv2LQAgNjYWdXV1sFqtuHjxIs6cOeNUJxE5jI7/ndm0ZWzG5mFsB2N/jevXrxMAkkgktGLFCoef7du3U21tLWk0GgJAnp6edPDgQcrKyqLQ0FAC4DCNfU5ODgEgjUZD/f39dOjQIRKLxdTQ0GBv09XVRTKZjLy9venBgwc0Pj5OOp2OiCanKg8JCXGqcWxsjKKiomZ8Tnl5eTQyMkK1tbX/4sow5ojDgi1qnZ2dBIDq6uqmbfMrLN6/f2/fZjabCQBduHCBiIjevHlDQqGQqqqqyGazERGRzWaj7Oxs8vf3p7GxMbJarRQTE0Oenp5kNBqd3ufTp09ThsXo6CjJZLIZn5NcLqfPnz/T1q1bZ7wPY65wnwVb1GQyGYKCghweQZlMJqf+hrVr1zqM+L1//z5EIhFSU1MBAIWFhThw4ADS0tLsj4CWLFmCS5cuwWq1orm5GTU1NXj06BFOnz6NDRs2ONXS1NTkMCPqbFksFmRnZ6OnpwdFRUUYGhrCkSNHMDEx8Y+PydgvvJ4FW9QEAgE8PDxw9uxZ1NTUoLGxEcPDw2hubsbmzZvt7V6/fo3KykpERkZCr9dDpVJBrVZj1apVGBoagl6vn3KdcQ8PD/j5+eHly5cQiUSQSqU4evTotPX8GkRmtVoxMDCAiYkJ3LhxA4ODg7BYLHB3d592X3d3dxAR1q9fj3fv3iE6OhpxcXEQCPgzIfv3OCwYw+S6BgEBATh27BgUCgXCwsKc2uzevdv++6lTp6BSqQAAOp0Oqamp8Pf3d9rn3r17+PjxI/bu3Yv6+nr8+PEDAwMD066TUl5ejtbWVvT19aG7uxvh4eHo6+uDt7f3b4NidHQUJpMJvb292L9/P0pLSyGTySCVSrmzm80J/sjBGICioiJotVrk5+dPGRQAkJeXhw8fPsBsNkOtVtv/CRsMBqfJ/YaGhnDu3DlkZGRAp9MhMDAQ6enp8PHxgVKpdHjM1d/fj6tXrwIAgoODUVJSgubmZvT29uLVq1fQaDQzOoempiY0NjZCr9fjxYsXEIlEPOkgmzN8Z8EYJpeHlUgkCAsLg0QiQVtbG75+/YqcnBwAk8uYFhQUQCQSOe0rl8tx8uRJSKVSeHh4oK2tDU+fPsXOnTvx5MkTe//E0qVLUVlZicTERMTFxUGpVMLd3R1ZWVnIzMyEj48PEhISEBUV5XD8mdwZiMViREZGQi6Xo6KiAg0NDfbaGZsLHBZs0Vu2bBmeP3+Ow4cP27etWbMGvr6+9nWcQ0NDpwwKAFAqlfDy8sK1a9cQEhKCpKQkFBcXY/Xq1U5to6Oj0dHRgYKCAuTn5wMAFAoF8vPzcffu3SmPv23bNuzbt8/leQgEApSUlACA/U6FsbnCg/LYojc8PIzBwUH7325ubg79D1VVVdDpdLhz584fraOlpQUTExPYsmXLH30fxv4JDgvGZoB4VDRb5LiDm7EZ4KBgix2HBWOMMZc4LBhjjLnEYcEYY8wlDgvGGGMucVgwxhhz6X/gCFXDdjm7KAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGq92F3mtPOC",
        "colab_type": "code",
        "outputId": "063b8d89-605e-4293-ee19-3db136b77c3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "model.save(\"resnet_bn_only_2\")\n",
        "!gsutil -m cp -r resnet_bn_only_2 gs://batch_norm"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: resnet_bn_only_2/assets\n",
            "Copying file://resnet_bn_only_2/saved_model.pb [Content-Type=application/octet-stream]...\n",
            "Copying file://resnet_bn_only_2/variables/variables.data-00000-of-00002 [Content-Type=application/octet-stream]...\n",
            "Copying file://resnet_bn_only_2/variables/variables.index [Content-Type=application/octet-stream]...\n",
            "Copying file://resnet_bn_only_2/variables/variables.data-00001-of-00002 [Content-Type=application/octet-stream]...\n",
            "/ [4/4 files][  3.9 MiB/  3.9 MiB] 100% Done                                    \n",
            "Operation completed over 4 objects/3.9 MiB.                                      \n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}